diff --git a/include/lc3.h b/include/lc3.h index 9e84ffb..058d0be 100644 --- a/include/lc3.h +++ b/include/lc3.h @@ -135,7 +135,7 @@ extern "C" { #define LC3_MIN_FRAME_BYTES 20 #define LC3_MAX_FRAME_BYTES 400 -#define LC3_MIN_FRAME_SAMPLES __LC3_NS( 7500, 8000) +#define LC3_MIN_FRAME_SAMPLES __LC3_NS( 2500, 8000) #define LC3_MAX_FRAME_SAMPLES __LC3_NS(10000, 48000) @@ -146,7 +146,8 @@ extern "C" { */ #define LC3_CHECK_DT_US(us) \ - ( ((us) == 7500) || ((us) == 10000) ) + ( ((us) == 2500) || ((us) == 5000) || \ + ((us) == 7500) || ((us) == 10000) ) #define LC3_CHECK_SR_HZ(sr) \ ( ((sr) == 8000) || ((sr) == 16000) || ((sr) == 24000) || \ @@ -196,7 +197,7 @@ typedef LC3_DECODER_MEM_T(10000, 48000) lc3_decoder_mem_48k_t; /** * Return the number of PCM samples in a frame - * dt_us Frame duration in us, 7500 or 10000 + * dt_us Frame duration in us, 2500, 5000, 7500 or 10000 * sr_hz Samplerate in Hz, 8000, 16000, 24000, 32000 or 48000 * return Number of PCM samples, -1 on bad parameters */ @@ -204,7 +205,7 @@ int lc3_frame_samples(int dt_us, int sr_hz); /** * Return the size of frames, from bitrate - * dt_us Frame duration in us, 7500 or 10000 + * dt_us Frame duration in us, 2500, 5000, 7500 or 10000 * bitrate Target bitrate in bit per second * return The floor size in bytes of the frames, -1 on bad parameters */ @@ -212,7 +213,7 @@ int lc3_frame_bytes(int dt_us, int bitrate); /** * Resolve the bitrate, from the size of frames - * dt_us Frame duration in us, 7500 or 10000 + * dt_us Frame duration in us, 2500, 5000, 7500 or 10000 * nbytes Size in bytes of the frames * return The according bitrate in bps, -1 on bad parameters */ @@ -220,7 +221,7 @@ int lc3_resolve_bitrate(int dt_us, int nbytes); /** * Return algorithmic delay, as a number of samples - * dt_us Frame duration in us, 7500 or 10000 + * dt_us Frame duration in us, 2500, 5000, 7500 or 10000 * sr_hz Samplerate in Hz, 8000, 16000, 24000, 32000 or 48000 * return Number of algorithmic delay samples, -1 on bad parameters */ @@ -228,7 +229,7 @@ int lc3_delay_samples(int dt_us, int sr_hz); /** * Return size needed for an encoder - * dt_us Frame duration in us, 7500 or 10000 + * dt_us Frame duration in us, 2500, 5000, 7500 or 10000 * sr_hz Samplerate in Hz, 8000, 16000, 24000, 32000 or 48000 * return Size of then encoder in bytes, 0 on bad parameters * @@ -239,7 +240,7 @@ unsigned lc3_encoder_size(int dt_us, int sr_hz); /** * Setup encoder - * dt_us Frame duration in us, 7500 or 10000 + * dt_us Frame duration in us, 2500, 5000, 7500 or 10000 * sr_hz Samplerate in Hz, 8000, 16000, 24000, 32000 or 48000 * sr_pcm_hz Input samplerate, downsampling option of input, or 0 * mem Encoder memory space, aligned to pointer type @@ -268,7 +269,7 @@ int lc3_encode(lc3_encoder_t encoder, enum lc3_pcm_format fmt, /** * Return size needed for an decoder - * dt_us Frame duration in us, 7500 or 10000 + * dt_us Frame duration in us, 2500, 5000, 7500 or 10000 * sr_hz Samplerate in Hz, 8000, 16000, 24000, 32000 or 48000 * return Size of then decoder in bytes, 0 on bad parameters * @@ -279,7 +280,7 @@ unsigned lc3_decoder_size(int dt_us, int sr_hz); /** * Setup decoder - * dt_us Frame duration in us, 7500 or 10000 + * dt_us Frame duration in us, 2500, 5000, 7500 or 10000 * sr_hz Samplerate in Hz, 8000, 16000, 24000, 32000 or 48000 * sr_pcm_hz Output samplerate, upsampling option of output (or 0) * mem Decoder memory space, aligned to pointer type diff --git a/include/lc3_private.h b/include/lc3_private.h index c4d6703..537337d 100644 --- a/include/lc3_private.h +++ b/include/lc3_private.h @@ -25,46 +25,55 @@ /** * Return number of samples, delayed samples and - * encoded spectrum coefficients within a frame - * - For encoding, keep 1.25 ms of temporal winodw + * encoded spectrum coefficients within a frame. + * + * - The number of MDCT delayed samples is the sum of half a frame and + * an ovelap of future by 1.25 ms (2.5ms, 5ms and 10ms frame durations) + * or 2 ms (7.5ms frame duration). + * + * - For encoding, keep 1.25 ms of temporal previous samples * - For decoding, keep 18 ms of history, aligned on frames, and a frame */ #define __LC3_NS(dt_us, sr_hz) \ - ( (dt_us * sr_hz) / 1000 / 1000 ) + ( (dt_us) * (sr_hz) / 1000 / 1000 ) #define __LC3_ND(dt_us, sr_hz) \ - ( (dt_us) == 7500 ? 23 * __LC3_NS(dt_us, sr_hz) / 30 \ - : 5 * __LC3_NS(dt_us, sr_hz) / 8 ) + ( __LC3_NS(dt_us, sr_hz) / 2 + \ + __LC3_NS((dt_us) == 7500 ? 2000 : 1250, sr_hz) ) #define __LC3_NT(sr_hz) \ - ( (5 * sr_hz) / 4000 ) + ( __LC3_NS(1250, sr_hz) ) #define __LC3_NH(dt_us, sr_hz) \ - ( ((3 - ((dt_us) >= 10000)) + 1) * __LC3_NS(dt_us, sr_hz) ) + ( __LC3_NS(18000, sr_hz) + 2*__LC3_NS(dt_us, sr_hz) - \ + (__LC3_NS(18000, sr_hz) % __LC3_NS(dt_us, sr_hz)) ) /** - * Frame duration 7.5ms or 10ms + * Frame duration */ enum lc3_dt { - LC3_DT_7M5, - LC3_DT_10M, + LC3_DT_2M5 = 0, + LC3_DT_5M = 1, + LC3_DT_7M5 = 2, + LC3_DT_10M = 3, LC3_NUM_DT }; + /** * Sampling frequency */ enum lc3_srate { - LC3_SRATE_8K, - LC3_SRATE_16K, - LC3_SRATE_24K, - LC3_SRATE_32K, - LC3_SRATE_48K, + LC3_SRATE_8K = 0, + LC3_SRATE_16K = 1, + LC3_SRATE_24K = 2, + LC3_SRATE_32K = 3, + LC3_SRATE_48K = 4, LC3_NUM_SRATE, }; diff --git a/src/attdet.c b/src/attdet.c index 3d1528d..9e6f08b 100644 --- a/src/attdet.c +++ b/src/attdet.c @@ -27,14 +27,14 @@ bool lc3_attdet_run(enum lc3_dt dt, enum lc3_srate sr, { /* --- Check enabling --- */ - const int nbytes_ranges[LC3_NUM_DT][LC3_NUM_SRATE - LC3_SRATE_32K][2] = { - [LC3_DT_7M5] = { { 61, 149 }, { 75, 149 } }, - [LC3_DT_10M] = { { 81, INT_MAX }, { 100, INT_MAX } }, + const int nbytes_ranges[][LC3_NUM_SRATE - LC3_SRATE_32K][2] = { + [LC3_DT_7M5 - LC3_DT_7M5] = { { 61, 149 }, { 75, 149 } }, + [LC3_DT_10M - LC3_DT_7M5] = { { 81, INT_MAX }, { 100, INT_MAX } }, }; - if (sr < LC3_SRATE_32K || - nbytes < nbytes_ranges[dt][sr - LC3_SRATE_32K][0] || - nbytes > nbytes_ranges[dt][sr - LC3_SRATE_32K][1] ) + if (dt < LC3_DT_7M5 || sr < LC3_SRATE_32K || + nbytes < nbytes_ranges[dt - LC3_DT_7M5][sr - LC3_SRATE_32K][0] || + nbytes > nbytes_ranges[dt - LC3_DT_7M5][sr - LC3_SRATE_32K][1] ) return 0; /* --- Filtering & Energy calculation --- */ diff --git a/src/attdet.h b/src/attdet.h index 14073bd..4d4ee17 100644 --- a/src/attdet.h +++ b/src/attdet.h @@ -16,13 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Time domain attack detector - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - */ - #ifndef __LC3_ATTDET_H #define __LC3_ATTDET_H diff --git a/src/bits.h b/src/bits.h index 5dd56cd..de65c93 100644 --- a/src/bits.h +++ b/src/bits.h @@ -17,8 +17,6 @@ ******************************************************************************/ /** - * LC3 - Bitstream management - * * The bitstream is written by the 2 ends of the buffer : * * - Arthmetic coder put bits while increasing memory addresses @@ -56,10 +54,6 @@ * - The procedure `lc3_check_bits()` returns indication that read has been * made crossing the other bit plane. * - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - * */ #ifndef __LC3_BITS_H diff --git a/src/bwdet.c b/src/bwdet.c index 8dc0f5c..aa815b1 100644 --- a/src/bwdet.c +++ b/src/bwdet.c @@ -25,29 +25,54 @@ enum lc3_bandwidth lc3_bwdet_run( enum lc3_dt dt, enum lc3_srate sr, const float *e) { - /* Bandwidth regions (Table 3.6) */ + /* Bandwidth regions */ struct region { int is : 8; int ie : 8; }; - static const struct region bws_table[LC3_NUM_DT] - [LC3_NUM_BANDWIDTH-1][LC3_NUM_BANDWIDTH-1] = { +#if !LC3_NPLUS - [LC3_DT_7M5] = { - { { 51, 63+1 } }, - { { 45, 55+1 }, { 58, 63+1 } }, - { { 42, 51+1 }, { 53, 58+1 }, { 60, 63+1 } }, - { { 40, 48+1 }, { 51, 55+1 }, { 57, 60+1 }, { 61, 63+1 } }, - }, + static const struct region bws_table_2m5[][LC3_NUM_BANDWIDTH-1] = { + { { 24, 34+1 } }, + { { 24, 32+1 }, { 35, 39+1 } }, + { { 24, 31+1 }, { 33, 38+1 }, { 39, 42+1 } }, + { { 22, 29+1 }, { 31, 35+1 }, { 37, 40+1 }, { 41, 43+1 } }, + }; - [LC3_DT_10M] = { - { { 53, 63+1 } }, - { { 47, 56+1 }, { 59, 63+1 } }, - { { 44, 52+1 }, { 54, 59+1 }, { 60, 63+1 } }, - { { 41, 49+1 }, { 51, 55+1 }, { 57, 60+1 }, { 61, 63+1 } }, - }, + static const struct region bws_table_5m[][LC3_NUM_BANDWIDTH-1] = { + { { 39, 49+1 } }, + { { 35, 44+1 }, { 47, 51+1 } }, + { { 34, 42+1 }, { 44, 49+1 }, { 50, 53+1 } }, + { { 32, 40+1 }, { 42, 46+1 }, { 48, 51+1 }, { 52, 54+1 } }, + }; + +#endif /* !LC3_NPLUS */ + + static const struct region bws_table_7m5[][LC3_NUM_BANDWIDTH-1] = { + { { 51, 63+1 } }, + { { 45, 55+1 }, { 58, 63+1 } }, + { { 42, 51+1 }, { 53, 58+1 }, { 60, 63+1 } }, + { { 40, 48+1 }, { 51, 55+1 }, { 57, 60+1 }, { 61, 63+1 } }, + }; + + static const struct region bws_table_10m[][LC3_NUM_BANDWIDTH-1] = { + { { 53, 63+1 } }, + { { 47, 56+1 }, { 59, 63+1 } }, + { { 44, 52+1 }, { 54, 59+1 }, { 60, 63+1 } }, + { { 41, 49+1 }, { 51, 55+1 }, { 57, 60+1 }, { 61, 63+1 } }, + }; + + static const struct region (*bws_table[])[LC3_NUM_BANDWIDTH-1] = { +#if !LC3_NPLUS + [LC3_DT_2M5] = bws_table_2m5, + [LC3_DT_5M ] = bws_table_5m, +#endif /* !LC3_NPLUS */ + [LC3_DT_7M5] = bws_table_7m5, + [LC3_DT_10M] = bws_table_10m, }; static const int l_table[LC3_NUM_DT][LC3_NUM_BANDWIDTH-1] = { + [LC3_DT_2M5] = { 4, 4, 3, 1 }, + [LC3_DT_5M ] = { 4, 4, 3, 1 }, [LC3_DT_7M5] = { 4, 4, 3, 2 }, [LC3_DT_10M] = { 4, 4, 3, 1 }, }; diff --git a/src/bwdet.h b/src/bwdet.h index 19039c7..773e6bb 100644 --- a/src/bwdet.h +++ b/src/bwdet.h @@ -16,13 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Bandwidth detector - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - */ - #ifndef __LC3_BWDET_H #define __LC3_BWDET_H @@ -31,7 +24,7 @@ /** - * Bandwidth detector (cf. 3.3.5) + * Bandwidth detector * dt, sr Duration and samplerate of the frame * e Energy estimation per bands * return Return detected bandwitdth diff --git a/src/common.h b/src/common.h index d60ad9e..60ae1fd 100644 --- a/src/common.h +++ b/src/common.h @@ -16,10 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Common constants and types - */ - #ifndef __LC3_COMMON_H #define __LC3_COMMON_H @@ -87,7 +83,7 @@ */ #define LC3_DT_US(dt) \ - ( (3 + (dt)) * 2500 ) + ( (1 + (dt)) * 2500 ) #define LC3_SRATE_KHZ(sr) \ ( (1 + (sr) + ((sr) == LC3_SRATE_48K)) * 8 ) @@ -96,19 +92,20 @@ /** * Return number of samples, delayed samples and * encoded spectrum coefficients within a frame - * - For encoding, keep 1.25 ms for temporal window + * + * - The number of MDCT delayed samples is the sum of half a frame and + * an ovelap of future by 1.25 ms (2.5ms, 5ms and 10ms frame durations) + * or 2 ms (7.5ms frame duration). + * + * - For encoding, keep 1.25 ms of temporal previous samples * - For decoding, keep 18 ms of history, aligned on frames, and a frame */ #define LC3_NS(dt, sr) \ - ( 20 * (3 + (dt)) * (1 + (sr) + ((sr) == LC3_SRATE_48K)) ) - -#define LC3_ND(dt, sr) \ - ( (dt) == LC3_DT_7M5 ? 23 * LC3_NS(dt, sr) / 30 \ - : 5 * LC3_NS(dt, sr) / 8 ) + ( 20 * (1 + (dt)) * (1 + (sr) + ((sr) == LC3_SRATE_48K)) ) #define LC3_NE(dt, sr) \ - ( 20 * (3 + (dt)) * (1 + (sr)) ) + ( 20 * (1 + (dt)) * (1 + (sr)) ) #define LC3_MAX_NS \ LC3_NS(LC3_DT_10M, LC3_SRATE_48K) @@ -116,11 +113,17 @@ #define LC3_MAX_NE \ LC3_NE(LC3_DT_10M, LC3_SRATE_48K) +#define LC3_ND(dt, sr) \ + ( LC3_NS(dt, sr) / 2 + \ + (5 + 3*((dt) == LC3_DT_7M5)) * LC3_SRATE_KHZ(sr) / 4 ) + #define LC3_NT(sr_hz) \ ( (5 * LC3_SRATE_KHZ(sr)) / 4 ) #define LC3_NH(dt, sr) \ - ( ((3 - dt) + 1) * LC3_NS(dt, sr) ) + ( ((dt == LC3_DT_2M5 ? 8 : \ + dt == LC3_DT_5M ? 4 : \ + dt == LC3_DT_7M5 ? 3 : 2) + 1) * LC3_NS(dt, sr) ) /** diff --git a/src/energy.c b/src/energy.c index bf86db7..21d5515 100644 --- a/src/energy.c +++ b/src/energy.c @@ -26,30 +26,17 @@ bool lc3_energy_compute( enum lc3_dt dt, enum lc3_srate sr, const float *x, float *e) { - static const int n1_table[LC3_NUM_DT][LC3_NUM_SRATE] = { - [LC3_DT_7M5] = { 56, 34, 27, 24, 22 }, - [LC3_DT_10M] = { 49, 28, 23, 20, 18 }, - }; + /* Mean the square of coefficients within each band */ - /* First bands are 1 coefficient width */ + int nb = lc3_num_bands[dt][sr]; + const uint16_t *lim = lc3_band_lim[dt][sr]; - int n1 = n1_table[dt][sr]; float e_sum[2] = { 0, 0 }; - int iband; + int iband_h = nb - (const int []){ + [LC3_DT_2M5] = 2, [LC3_DT_5M ] = 3, + [LC3_DT_7M5] = 4, [LC3_DT_10M] = 2 }[dt]; - for (iband = 0; iband < n1; iband++) { - *e = x[iband] * x[iband]; - e_sum[0] += *(e++); - } - - /* Mean the square of coefficients within each band, - * note that 7.5ms 8KHz frame has more bands than samples */ - - int nb = LC3_MIN(LC3_NUM_BANDS, LC3_NS(dt, sr)); - int iband_h = nb - 2*(2 - dt); - const int *lim = lc3_band_lim[dt][sr]; - - for (int i = lim[iband]; iband < nb; iband++) { + for (int iband = 0, i = lim[iband]; iband < nb; iband++) { int ie = lim[iband+1]; int n = ie - i; @@ -61,9 +48,6 @@ bool lc3_energy_compute( e_sum[iband >= iband_h] += *(e++); } - for (; iband < LC3_NUM_BANDS; iband++) - *(e++) = 0; - /* Return the near nyquist flag */ return e_sum[1] > 30 * e_sum[0]; diff --git a/src/energy.h b/src/energy.h index 39f0124..8ebd726 100644 --- a/src/energy.h +++ b/src/energy.h @@ -16,13 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Energy estimation per band - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - */ - #ifndef __LC3_ENERGY_H #define __LC3_ENERGY_H diff --git a/src/fastmath.h b/src/fastmath.h index 4210f2e..5ff7081 100644 --- a/src/fastmath.h +++ b/src/fastmath.h @@ -16,10 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Mathematics function approximation - */ - #ifndef __LC3_FASTMATH_H #define __LC3_FASTMATH_H diff --git a/src/lc3.c b/src/lc3.c index 6f54300..090ec30 100644 --- a/src/lc3.c +++ b/src/lc3.c @@ -57,7 +57,9 @@ struct side_data { */ static enum lc3_dt resolve_dt(int us) { - return us == 7500 ? LC3_DT_7M5 : + return us == 2500 ? LC3_DT_2M5 : + us == 5000 ? LC3_DT_5M : + us == 7500 ? LC3_DT_7M5 : us == 10000 ? LC3_DT_10M : LC3_NUM_DT; } @@ -269,7 +271,7 @@ static void analyze(struct lc3_encoder *encoder, /* --- Spectral --- */ - float e[LC3_NUM_BANDS]; + float e[LC3_MAX_BANDS]; lc3_mdct_forward(dt, sr_pcm, sr, xs, xd, xf); @@ -534,7 +536,8 @@ static int decode(struct lc3_decoder *decoder, if ((ret = lc3_spec_get_side(&bits, dt, sr, &side->spec)) < 0) return ret; - lc3_tns_get_data(&bits, dt, side->bw, nbytes, &side->tns); + if ((ret = lc3_tns_get_data(&bits, dt, side->bw, nbytes, &side->tns)) < 0) + return ret; side->pitch_present = lc3_get_bit(&bits); diff --git a/src/ltpf.c b/src/ltpf.c index a0cb7ba..8e538e8 100644 --- a/src/ltpf.c +++ b/src/ltpf.c @@ -457,7 +457,7 @@ LC3_HOT static int argmax_weighted( } /** - * Interpolate from pitch detected value (3.3.9.8) + * Interpolate from pitch detected value * x, n [-2..-1] Previous, [0..n] Current input * d The phase of interpolation (0 to 3) * return The interpolated vector @@ -492,7 +492,7 @@ LC3_HOT static void interpolate(const int16_t *x, int n, int d, int16_t *y) } /** - * Interpolate autocorrelation (3.3.9.7) + * Interpolate autocorrelation * x [-4..-1] Previous, [0..4] Current input * d The phase of interpolation (-3 to 3) * return The interpolated value @@ -522,7 +522,7 @@ LC3_HOT static float interpolate_corr(const float *x, int d) } /** - * Pitch detection algorithm (3.3.9.5-6) + * Pitch detection algorithm * ltpf Context of analysis * x, n [-114..-17] Previous, [0..n-1] Current 6.4KHz samples * tc Return the pitch-lag estimation @@ -530,8 +530,8 @@ LC3_HOT static float interpolate_corr(const float *x, int d) * * The `x` vector is aligned on 32 bits */ -static bool detect_pitch( - struct lc3_ltpf_analysis *ltpf, const int16_t *x, int n, int *tc) +static bool detect_pitch(struct lc3_ltpf_analysis *ltpf, + const int16_t *x, int n, int *tc) { float rm1, rm2; float r[98]; @@ -562,7 +562,7 @@ static bool detect_pitch( } /** - * Pitch-lag parameter (3.3.9.7) + * Pitch-lag parameter * x, n [-232..-28] Previous, [0..n-1] Current 12.8KHz samples, Q14 * tc Pitch-lag estimation * pitch The pitch value, in fixed .4 @@ -615,7 +615,7 @@ bool lc3_ltpf_analyse( /* --- Resampling to 12.8 KHz --- */ int z_12k8 = sizeof(ltpf->x_12k8) / sizeof(*ltpf->x_12k8); - int n_12k8 = dt == LC3_DT_7M5 ? 96 : 128; + int n_12k8 = (1 + dt) * 32; memmove(ltpf->x_12k8, ltpf->x_12k8 + n_12k8, (z_12k8 - n_12k8) * sizeof(*ltpf->x_12k8)); @@ -624,7 +624,7 @@ bool lc3_ltpf_analyse( resample_12k8[sr](<pf->hp50, x, x_12k8, n_12k8); - x_12k8 -= (dt == LC3_DT_7M5 ? 44 : 24); + x_12k8 -= (dt == LC3_DT_7M5 ? 44 : 24); /* --- Resampling to 6.4 KHz --- */ @@ -638,6 +638,13 @@ bool lc3_ltpf_analyse( resample_6k4(x_12k8, x_6k4, n_6k4); + /* --- Enlarge for small frame size --- */ + + if (dt == LC3_DT_2M5) { + x_12k8 -= n_12k8, x_6k4 -= n_6k4; + n_12k8 += n_12k8; n_6k4 += n_6k4; + } + /* --- Pitch detection --- */ int tc, pitch = 0; @@ -817,6 +824,11 @@ void lc3_ltpf_synthesize(enum lc3_dt dt, enum lc3_srate sr, int nbytes, pitch = (pitch * LC3_SRATE_KHZ(sr) * 10 + 64) / 128; int nbits = (nbytes*8 * 10000 + (dt_us/2)) / dt_us; + if (dt == LC3_DT_2M5) + nbits = (6 * nbits + 5) / 10; + if (dt == LC3_DT_5M) + nbits -= 160; + int g_idx = LC3_MAX(nbits / 80, 3 + (int)sr) - (3 + sr); bool active = data && data->active && g_idx < 4; @@ -832,30 +844,31 @@ void lc3_ltpf_synthesize(enum lc3_dt dt, enum lc3_srate sr, int nbytes, /* --- Transition handling --- */ int ns = LC3_NS(dt, sr); - int nt = ns / (3 + dt); - float x0[MAX_FILTER_WIDTH]; + int nt = ns / (1 + dt); + float x0[2][MAX_FILTER_WIDTH]; + + memcpy(x0[0], ltpf->x, (w-1) * sizeof(float)); + memcpy(ltpf->x, x + ns - (w-1), (w-1) * sizeof(float)); if (active) - memcpy(x0, x + nt-(w-1), (w-1) * sizeof(float)); + memcpy(x0[1], x + nt-(w-1), (w-1) * sizeof(float)); if (!ltpf->active && active) - synthesize[sr](xh, nh, pitch/4, ltpf->x, x, nt, c, 1); + synthesize[sr](xh, nh, pitch/4, x0[0], x, nt, c, 1); else if (ltpf->active && !active) - synthesize[sr](xh, nh, ltpf->pitch/4, ltpf->x, x, nt, ltpf->c, -1); + synthesize[sr](xh, nh, ltpf->pitch/4, x0[0], x, nt, ltpf->c, -1); else if (ltpf->active && active && ltpf->pitch == pitch) - synthesize[sr](xh, nh, pitch/4, ltpf->x, x, nt, c, 0); + synthesize[sr](xh, nh, pitch/4, x0[0], x, nt, c, 0); else if (ltpf->active && active) { - synthesize[sr](xh, nh, ltpf->pitch/4, ltpf->x, x, nt, ltpf->c, -1); + synthesize[sr](xh, nh, ltpf->pitch/4, x0[0], x, nt, ltpf->c, -1); synthesize[sr](xh, nh, pitch/4, (x <= xh ? x + nh : x) - (w-1), x, nt, c, 1); } /* --- Remainder --- */ - memcpy(ltpf->x, x + ns - (w-1), (w-1) * sizeof(float)); - - if (active) - synthesize[sr](xh, nh, pitch/4, x0, x + nt, ns-nt, c, 0); + if (active && ns > nt) + synthesize[sr](xh, nh, pitch/4, x0[1], x + nt, ns-nt, c, 0); /* --- Update state --- */ diff --git a/src/ltpf.h b/src/ltpf.h index 0d5bb3c..42cedc6 100644 --- a/src/ltpf.h +++ b/src/ltpf.h @@ -16,13 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Long Term Postfilter - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - */ - #ifndef __LC3_LTPF_H #define __LC3_LTPF_H diff --git a/src/mdct.c b/src/mdct.c index f598221..0fd6634 100644 --- a/src/mdct.c +++ b/src/mdct.c @@ -175,9 +175,9 @@ static struct lc3_complex *fft(const struct lc3_complex *x, int n, * * n = 5^1 * 3^n3 * 2^n2 * - * for n = 40, 80, 160 n3 = 0, n2 = [3..5] - * n = 30, 60, 120, 240 n3 = 1, n2 = [1..4] - * n = 90, 180 n3 = 2, n2 = [1..2] + * for n = 10, 20, 40, 80, 160 n3 = 0, n2 = [1..5] + * n = 30, 60, 120, 240 n3 = 1, n2 = [1..4] + * n = 90, 180 n3 = 2, n2 = [1..2] * * Note that the expression `n & (n-1) == 0` is equivalent * to the check that `n` is a power of 2. */ diff --git a/src/mdct.h b/src/mdct.h index 03ae801..3e23a1f 100644 --- a/src/mdct.h +++ b/src/mdct.h @@ -16,13 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Compute LD-MDCT (Low Delay Modified Discret Cosinus Transform) - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - */ - #ifndef __LC3_MDCT_H #define __LC3_MDCT_H diff --git a/src/plc.h b/src/plc.h index 6fda5b5..a7990bd 100644 --- a/src/plc.h +++ b/src/plc.h @@ -16,13 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Packet Loss Concealment - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - */ - #ifndef __LC3_PLC_H #define __LC3_PLC_H diff --git a/src/sns.c b/src/sns.c index 56a893c..284f307 100644 --- a/src/sns.c +++ b/src/sns.c @@ -157,7 +157,7 @@ LC3_HOT static void compute_scale_factors( /* Pre-emphasis gain table : * Ge[b] = 10 ^ (b * g_tilt) / 630 , b = [0..63] */ - static const float ge_table[LC3_NUM_SRATE][LC3_NUM_BANDS] = { + static const float ge_table[LC3_NUM_SRATE][LC3_MAX_BANDS] = { [LC3_SRATE_8K] = { /* g_tilt = 14 */ 1.00000000e+00, 1.05250029e+00, 1.10775685e+00, 1.16591440e+00, @@ -250,17 +250,22 @@ LC3_HOT static void compute_scale_factors( 7.19685673e+02, 8.03085722e+02, 8.96150502e+02, 1.00000000e+03 }, }; - float e[LC3_NUM_BANDS]; + float e[LC3_MAX_BANDS]; /* --- Copy and padding --- */ - int nb = LC3_MIN(lc3_band_lim[dt][sr][LC3_NUM_BANDS], LC3_NUM_BANDS); - int n2 = LC3_NUM_BANDS - nb; + int nb = lc3_num_bands[dt][sr]; + int n4 = nb < 32 ? 32 % nb : 0; + int n2 = nb < 32 ? nb - n4 : LC3_MAX_BANDS - nb; - for (int i2 = 0; i2 < n2; i2++) - e[2*i2 + 0] = e[2*i2 + 1] = eb[i2]; + for (int i4 = 0; i4 < n4; i4++) + e[4*i4 + 0] = e[4*i4 + 1] = + e[4*i4 + 2] = e[4*i4 + 3] = eb[i4]; - memcpy(e + 2*n2, eb + n2, (nb - n2) * sizeof(float)); + for (int i2 = n4; i2 < n4+n2; i2++) + e[2*(n4+i2) + 0] = e[2*(n4+i2) + 1] = eb[i2]; + + memcpy(e + 4*n4 + 2*n2, eb + n4 + n2, (nb - n4 - n2) * sizeof(float)); /* --- Smoothing, pre-emphasis and logarithm --- */ @@ -269,7 +274,7 @@ LC3_HOT static void compute_scale_factors( float e0 = e[0], e1 = e[0], e2; float e_sum = 0; - for (int i = 0; i < LC3_NUM_BANDS-1; ) { + for (int i = 0; i < LC3_MAX_BANDS-1; ) { e[i] = (e0 * 0.25f + e1 * 0.5f + (e2 = e[i+1]) * 0.25f) * ge[i]; e_sum += e[i++]; @@ -280,12 +285,12 @@ LC3_HOT static void compute_scale_factors( e_sum += e[i++]; } - e[LC3_NUM_BANDS-1] = (e0 * 0.25f + e1 * 0.75f) * ge[LC3_NUM_BANDS-1]; - e_sum += e[LC3_NUM_BANDS-1]; + e[LC3_MAX_BANDS-1] = (e0 * 0.25f + e1 * 0.75f) * ge[LC3_MAX_BANDS-1]; + e_sum += e[LC3_MAX_BANDS-1]; float noise_floor = fmaxf(e_sum * (1e-4f / 64), 0x1p-32f); - for (int i = 0; i < LC3_NUM_BANDS; i++) + for (int i = 0; i < LC3_MAX_BANDS; i++) e[i] = fast_log2f(fmaxf(e[i], noise_floor)) * 0.5f; /* --- Grouping & scaling --- */ @@ -680,7 +685,7 @@ LC3_HOT static void spectral_shaping(enum lc3_dt dt, enum lc3_srate sr, { /* --- Interpolate scale factors --- */ - float scf[LC3_NUM_BANDS]; + float scf[LC3_MAX_BANDS]; float s0, s1 = inv ? -scf_q[0] : scf_q[0]; scf[0] = scf[1] = s1; @@ -694,18 +699,22 @@ LC3_HOT static void spectral_shaping(enum lc3_dt dt, enum lc3_srate sr, scf[62] = s1 + 0.125f * (s1 - s0); scf[63] = s1 + 0.375f * (s1 - s0); - int nb = LC3_MIN(lc3_band_lim[dt][sr][LC3_NUM_BANDS], LC3_NUM_BANDS); - int n2 = LC3_NUM_BANDS - nb; + int nb = lc3_num_bands[dt][sr]; + int n4 = nb < 32 ? 32 % nb : 0; + int n2 = nb < 32 ? nb - n4 : LC3_MAX_BANDS - nb; - for (int i2 = 0; i2 < n2; i2++) - scf[i2] = 0.5f * (scf[2*i2] + scf[2*i2+1]); + for (int i4 = 0; i4 < n4; i4++) + scf[i4] = 0.25f * (scf[4*i4+0] + scf[4*i4+1] + + scf[4*i4+2] + scf[4*i4+3]); - if (n2 > 0) - memmove(scf + n2, scf + 2*n2, (nb - n2) * sizeof(float)); + for (int i2 = n4; i2 < n4+n2; i2++) + scf[i2] = 0.5f * (scf[2*(n4+i2)] + scf[2*(n4+i2)+1]); + + memmove(scf + n4 + n2, scf + 4*n4 + 2*n2, (nb - n4 - n2) * sizeof(float)); /* --- Spectral shaping --- */ - const int *lim = lc3_band_lim[dt][sr]; + const uint16_t *lim = lc3_band_lim[dt][sr]; for (int i = 0, ib = 0; ib < nb; ib++) { float g_sns = fast_exp2f(-scf[ib]); diff --git a/src/sns.h b/src/sns.h index 432223c..8188f4a 100644 --- a/src/sns.h +++ b/src/sns.h @@ -16,13 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Spectral Noise Shaping - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - */ - #ifndef __LC3_SNS_H #define __LC3_SNS_H diff --git a/src/spec.c b/src/spec.c index f857f47..ebf49ff 100644 --- a/src/spec.c +++ b/src/spec.c @@ -666,13 +666,13 @@ LC3_HOT static void get_lsb(lc3_bits_t *bits, LC3_HOT static int estimate_noise(enum lc3_dt dt, enum lc3_bandwidth bw, const uint16_t *xq, int nq, const float *x) { - int bw_stop = (dt == LC3_DT_7M5 ? 60 : 80) * (1 + bw); - int w = 2 + dt; + int bw_stop = 20 * (1 + dt) * (1 + bw); + int w = 1 + (dt >= LC3_DT_7M5) + (dt>= LC3_DT_10M); float sum = 0; int i, n = 0, z = 0; - for (i = 6*(3 + dt) - w; i < LC3_MIN(nq, bw_stop); i++) { + for (i = 6 * (1 + dt) - w; i < LC3_MIN(nq, bw_stop); i++) { z = xq[i] ? 0 : z + 1; if (z > 2*w) sum += fabsf(x[i - w]), n++; @@ -682,7 +682,7 @@ LC3_HOT static int estimate_noise(enum lc3_dt dt, enum lc3_bandwidth bw, if (++z > 2*w) sum += fabsf(x[i - w]), n++; - int nf = n ? 8 - (int)((16 * sum) / n + 0.5f) : 0; + int nf = n ? 8 - (int)((16 * sum) / n + 0.5f) : 8; return LC3_CLIP(nf, 0, 7); } @@ -697,13 +697,13 @@ LC3_HOT static int estimate_noise(enum lc3_dt dt, enum lc3_bandwidth bw, LC3_HOT static void fill_noise(enum lc3_dt dt, enum lc3_bandwidth bw, int nf, uint16_t nf_seed, float g, float *x, int nq) { - int bw_stop = (dt == LC3_DT_7M5 ? 60 : 80) * (1 + bw); - int w = 2 + dt; + int bw_stop = 20 * (1 + dt) * (1 + bw); + int w = 1 + (dt >= LC3_DT_7M5) + (dt>= LC3_DT_10M); float s = g * (float)(8 - nf) / 16; int i, z = 0; - for (i = 6*(3 + dt) - w; i < LC3_MIN(nq, bw_stop); i++) { + for (i = 6 * (1 + dt) - w; i < LC3_MIN(nq, bw_stop); i++) { z = x[i] ? 0 : z + 1; if (z > 2*w) { nf_seed = (13849 + nf_seed*31821) & 0xffff; diff --git a/src/spec.h b/src/spec.h index 091d25f..95c00d1 100644 --- a/src/spec.h +++ b/src/spec.h @@ -16,13 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Spectral coefficients encoding/decoding - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - */ - #ifndef __LC3_SPEC_H #define __LC3_SPEC_H diff --git a/src/tables.c b/src/tables.c index c498b5e..3e82d33 100644 --- a/src/tables.c +++ b/src/tables.c @@ -438,6 +438,35 @@ const struct lc3_fft_bf2_twiddles *lc3_fft_twiddles_bf2[][3] = { * W[n] = e * sqrt( sqrt( 4/N ) ), n = [0..N/4-1] */ +#if !LC3_NPLUS + +static const struct lc3_mdct_rot_def mdct_rot_40 = { + .n4 = 40/4, .w = (const struct lc3_complex []){ + { 5.6223293e-01, 1.1040837e-02 }, { 5.5358374e-01, 9.8857513e-02 }, + { 5.3130348e-01, 1.8423999e-01 }, { 4.9594077e-01, 2.6508587e-01 }, + { 4.4836635e-01, 3.3940445e-01 }, { 3.8975167e-01, 4.0536577e-01 }, + { 3.2154000e-01, 4.6134563e-01 }, { 2.4541095e-01, 5.0596564e-01 }, + { 1.6323907e-01, 5.3812710e-01 }, { 7.7047702e-02, 5.5703808e-01 }, + } +}; + +static const struct lc3_mdct_rot_def mdct_rot_80 = { + .n4 = 80/4, .w = (const struct lc3_complex []){ + { 4.7284802e-01, 4.6423237e-03 }, { 4.7102615e-01, 4.1727241e-02 }, + { 4.6630025e-01, 7.8554895e-02 }, { 4.5869946e-01, 1.1489823e-01 }, + { 4.4827063e-01, 1.5053319e-01 }, { 4.3507806e-01, 1.8524005e-01 }, + { 4.1920310e-01, 2.1880485e-01 }, { 4.0074360e-01, 2.5102064e-01 }, + { 3.7981339e-01, 2.8168881e-01 }, { 3.5654150e-01, 3.1062027e-01 }, + { 3.3107142e-01, 3.3763666e-01 }, { 3.0356017e-01, 3.6257140e-01 }, + { 2.7417737e-01, 3.8527077e-01 }, { 2.4310418e-01, 4.0559482e-01 }, + { 2.1053217e-01, 4.2341824e-01 }, { 1.7666215e-01, 4.3863114e-01 }, + { 1.4170296e-01, 4.5113974e-01 }, { 1.0587012e-01, 4.6086692e-01 }, + { 6.9384558e-02, 4.6775269e-01 }, { 3.2471215e-02, 4.7175462e-01 }, + } +}; + +#endif /* !LC3_NPLUS */ + static const struct lc3_mdct_rot_def mdct_rot_120 = { .n4 = 120/4, .w = (const struct lc3_complex []){ { 4.2727785e-01, 2.7965670e-03 }, { 4.2654592e-01, 2.5154729e-02 }, @@ -984,18 +1013,949 @@ static const struct lc3_mdct_rot_def mdct_rot_960 = { }; const struct lc3_mdct_rot_def * lc3_mdct_rot[LC3_NUM_DT][LC3_NUM_SRATE] = { + +#if !LC3_NPLUS + + [LC3_DT_2M5] = { &mdct_rot_40 , &mdct_rot_80 , &mdct_rot_120, + &mdct_rot_160, &mdct_rot_240 }, + + [LC3_DT_5M ] = { &mdct_rot_80 , &mdct_rot_160, &mdct_rot_240, + &mdct_rot_320, &mdct_rot_480 }, + +#endif /* !LC3_NPLUS */ + [LC3_DT_7M5] = { &mdct_rot_120, &mdct_rot_240, &mdct_rot_360, &mdct_rot_480, &mdct_rot_720 }, + [LC3_DT_10M] = { &mdct_rot_160, &mdct_rot_320, &mdct_rot_480, &mdct_rot_640, &mdct_rot_960 } }; /** - * Low delay MDCT windows (cf. 3.7.3) + * Low delay MDCT windows */ -static const float mdct_win_10m_80[80+50] = { +#define __LC3_MDCT_WIN_LEN(_dt, _sr) \ + ( LC3_NS(LC3_DT_ ## _dt, LC3_SRATE_ ## _sr) + \ + LC3_ND(LC3_DT_ ## _dt, LC3_SRATE_ ## _sr) ) + +#if !LC3_NPLUS + +static const float mdct_win_2m5_8k[__LC3_MDCT_WIN_LEN(2M5, 8K)] = { + 6.73791440e-03, 2.73228958e-02, 6.16356097e-02, 1.11912504e-01, + 1.78705350e-01, 2.60752499e-01, 3.54977638e-01, 4.56769675e-01, + 5.60523927e-01, 6.60366535e-01, 7.50943422e-01, 8.28138232e-01, + 8.89585018e-01, 9.34874773e-01, 9.65405703e-01, 9.83902633e-01, + 9.93718088e-01, 9.98098731e-01, 9.99626637e-01, 9.99977291e-01, + 9.99977291e-01, 9.99626637e-01, 9.98098731e-01, 9.93718088e-01, + 9.83902633e-01, 9.65405703e-01, 9.34874773e-01, 8.89585018e-01, + 8.28138232e-01, 7.50943422e-01, 6.60366535e-01, 5.60523927e-01, + 4.56769675e-01, 3.54977638e-01, 2.60752499e-01, 1.78705350e-01, + 1.11912504e-01, 6.16356097e-02, 2.73228958e-02, 6.73791440e-03, +}; + +static const float mdct_win_2m5_16k[__LC3_MDCT_WIN_LEN(2M5, 16K)] = { + 4.76441615e-03, 1.20463628e-02, 2.22639654e-02, 3.58022311e-02, + 5.29905465e-02, 7.40851840e-02, 9.92538592e-02, 1.28563118e-01, + 1.61969244e-01, 1.99313241e-01, 2.40320282e-01, 2.84603818e-01, + 3.31674323e-01, 3.80952458e-01, 4.31786202e-01, 4.83471316e-01, + 5.35274327e-01, 5.86457098e-01, 6.36301916e-01, 6.84136019e-01, + 7.29354445e-01, 7.71440129e-01, 8.09980291e-01, 8.44678279e-01, + 8.75360204e-01, 9.01975984e-01, 9.24594627e-01, 9.43393949e-01, + 9.58645225e-01, 9.70693650e-01, 9.79935831e-01, 9.86795807e-01, + 9.91701328e-01, 9.95062145e-01, 9.97251917e-01, 9.98595014e-01, + 9.99358895e-01, 9.99752127e-01, 9.99927440e-01, 9.99988650e-01, + 9.99988650e-01, 9.99927440e-01, 9.99752127e-01, 9.99358895e-01, + 9.98595014e-01, 9.97251917e-01, 9.95062145e-01, 9.91701328e-01, + 9.86795807e-01, 9.79935831e-01, 9.70693650e-01, 9.58645225e-01, + 9.43393949e-01, 9.24594627e-01, 9.01975984e-01, 8.75360204e-01, + 8.44678279e-01, 8.09980291e-01, 7.71440129e-01, 7.29354445e-01, + 6.84136019e-01, 6.36301916e-01, 5.86457098e-01, 5.35274327e-01, + 4.83471316e-01, 4.31786202e-01, 3.80952458e-01, 3.31674323e-01, + 2.84603818e-01, 2.40320282e-01, 1.99313241e-01, 1.61969244e-01, + 1.28563118e-01, 9.92538592e-02, 7.40851840e-02, 5.29905465e-02, + 3.58022311e-02, 2.22639654e-02, 1.20463628e-02, 4.76441615e-03, +}; + +static const float mdct_win_2m5_24k[__LC3_MDCT_WIN_LEN(2M5, 24K)] = { + 3.89013421e-03, 8.20259508e-03, 1.37023556e-02, 2.05296853e-02, + 2.88030773e-02, 3.86278514e-02, 5.00956972e-02, 6.32829654e-02, + 7.82488109e-02, 9.50334651e-02, 1.13656749e-01, 1.34116888e-01, + 1.56389671e-01, 1.80427981e-01, 2.06161727e-01, 2.33498181e-01, + 2.62322720e-01, 2.92499991e-01, 3.23875450e-01, 3.56277288e-01, + 3.89518694e-01, 4.23400416e-01, 4.57713594e-01, 4.92242792e-01, + 5.26769190e-01, 5.61073868e-01, 5.94941120e-01, 6.28161737e-01, + 6.60536189e-01, 6.91877646e-01, 7.22014766e-01, 7.50794208e-01, + 7.78082793e-01, 8.03769285e-01, 8.27765737e-01, 8.50008365e-01, + 8.70457945e-01, 8.89099694e-01, 9.05942652e-01, 9.21018560e-01, + 9.34380273e-01, 9.46099727e-01, 9.56265526e-01, 9.64980202e-01, + 9.72357239e-01, 9.78517931e-01, 9.83588198e-01, 9.87695434e-01, + 9.90965519e-01, 9.93520077e-01, 9.95474078e-01, 9.96933861e-01, + 9.97995624e-01, 9.98744422e-01, 9.99253666e-01, 9.99585105e-01, + 9.99789244e-01, 9.99906118e-01, 9.99966358e-01, 9.99992433e-01, + 9.99992433e-01, 9.99966358e-01, 9.99906118e-01, 9.99789244e-01, + 9.99585105e-01, 9.99253666e-01, 9.98744422e-01, 9.97995624e-01, + 9.96933861e-01, 9.95474078e-01, 9.93520077e-01, 9.90965519e-01, + 9.87695434e-01, 9.83588198e-01, 9.78517931e-01, 9.72357239e-01, + 9.64980202e-01, 9.56265526e-01, 9.46099727e-01, 9.34380273e-01, + 9.21018560e-01, 9.05942652e-01, 8.89099694e-01, 8.70457945e-01, + 8.50008365e-01, 8.27765737e-01, 8.03769285e-01, 7.78082793e-01, + 7.50794208e-01, 7.22014766e-01, 6.91877646e-01, 6.60536189e-01, + 6.28161737e-01, 5.94941120e-01, 5.61073868e-01, 5.26769190e-01, + 4.92242792e-01, 4.57713594e-01, 4.23400416e-01, 3.89518694e-01, + 3.56277288e-01, 3.23875450e-01, 2.92499991e-01, 2.62322720e-01, + 2.33498181e-01, 2.06161727e-01, 1.80427981e-01, 1.56389671e-01, + 1.34116888e-01, 1.13656749e-01, 9.50334651e-02, 7.82488109e-02, + 6.32829654e-02, 5.00956972e-02, 3.86278514e-02, 2.88030773e-02, + 2.05296853e-02, 1.37023556e-02, 8.20259508e-03, 3.89013421e-03, +}; + +static const float mdct_win_2m5_32k[__LC3_MDCT_WIN_LEN(2M5, 32K)] = { + 3.36895835e-03, 6.45555741e-03, 1.01430808e-02, 1.45212685e-02, + 1.96507673e-02, 2.55835280e-02, 3.23662853e-02, 4.00411787e-02, + 4.86456418e-02, 5.82120708e-02, 6.87674290e-02, 8.03328498e-02, + 9.29232660e-02, 1.06547081e-01, 1.21205893e-01, 1.36894275e-01, + 1.53599623e-01, 1.71302066e-01, 1.89974455e-01, 2.09582413e-01, + 2.30084468e-01, 2.51432255e-01, 2.73570790e-01, 2.96438816e-01, + 3.19969212e-01, 3.44089476e-01, 3.68722258e-01, 3.93785956e-01, + 4.19195353e-01, 4.44862309e-01, 4.70696475e-01, 4.96606050e-01, + 5.22498546e-01, 5.48281575e-01, 5.73863636e-01, 5.99154896e-01, + 6.24067964e-01, 6.48518633e-01, 6.72426605e-01, 6.95716161e-01, + 7.18316799e-01, 7.40163807e-01, 7.61198780e-01, 7.81370064e-01, + 8.00633131e-01, 8.18950870e-01, 8.36293796e-01, 8.52640176e-01, + 8.67976054e-01, 8.82295205e-01, 8.95598976e-01, 9.07896060e-01, + 9.19202165e-01, 9.29539615e-01, 9.38936863e-01, 9.47427941e-01, + 9.55051846e-01, 9.61851871e-01, 9.67874900e-01, 9.73170662e-01, + 9.77790986e-01, 9.81789034e-01, 9.85218555e-01, 9.88133167e-01, + 9.90585664e-01, 9.92627388e-01, 9.94307658e-01, 9.95673273e-01, + 9.96768094e-01, 9.97632718e-01, 9.98304240e-01, 9.98816100e-01, + 9.99198030e-01, 9.99476075e-01, 9.99672688e-01, 9.99806905e-01, + 9.99894561e-01, 9.99948558e-01, 9.99979163e-01, 9.99994325e-01, + 9.99994325e-01, 9.99979163e-01, 9.99948558e-01, 9.99894561e-01, + 9.99806905e-01, 9.99672688e-01, 9.99476075e-01, 9.99198030e-01, + 9.98816100e-01, 9.98304240e-01, 9.97632718e-01, 9.96768094e-01, + 9.95673273e-01, 9.94307658e-01, 9.92627388e-01, 9.90585664e-01, + 9.88133167e-01, 9.85218555e-01, 9.81789034e-01, 9.77790986e-01, + 9.73170662e-01, 9.67874900e-01, 9.61851871e-01, 9.55051846e-01, + 9.47427941e-01, 9.38936863e-01, 9.29539615e-01, 9.19202165e-01, + 9.07896060e-01, 8.95598976e-01, 8.82295205e-01, 8.67976054e-01, + 8.52640176e-01, 8.36293796e-01, 8.18950870e-01, 8.00633131e-01, + 7.81370064e-01, 7.61198780e-01, 7.40163807e-01, 7.18316799e-01, + 6.95716161e-01, 6.72426605e-01, 6.48518633e-01, 6.24067964e-01, + 5.99154896e-01, 5.73863636e-01, 5.48281575e-01, 5.22498546e-01, + 4.96606050e-01, 4.70696475e-01, 4.44862309e-01, 4.19195353e-01, + 3.93785956e-01, 3.68722258e-01, 3.44089476e-01, 3.19969212e-01, + 2.96438816e-01, 2.73570790e-01, 2.51432255e-01, 2.30084468e-01, + 2.09582413e-01, 1.89974455e-01, 1.71302066e-01, 1.53599623e-01, + 1.36894275e-01, 1.21205893e-01, 1.06547081e-01, 9.29232660e-02, + 8.03328498e-02, 6.87674290e-02, 5.82120708e-02, 4.86456418e-02, + 4.00411787e-02, 3.23662853e-02, 2.55835280e-02, 1.96507673e-02, + 1.45212685e-02, 1.01430808e-02, 6.45555741e-03, 3.36895835e-03, +}; + +static const float mdct_win_2m5_48k[__LC3_MDCT_WIN_LEN(2M5, 48K)] = { + 2.75074638e-03, 4.77524515e-03, 6.99126548e-03, 9.47011816e-03, + 1.22441576e-02, 1.53355947e-02, 1.87626677e-02, 2.25415434e-02, + 2.66870142e-02, 3.12127707e-02, 3.61315008e-02, 4.14549100e-02, + 4.71937052e-02, 5.33575588e-02, 5.99550649e-02, 6.69936920e-02, + 7.44797342e-02, 8.24182658e-02, 9.08130967e-02, 9.96667332e-02, + 1.08980342e-01, 1.18753719e-01, 1.28985265e-01, 1.39671964e-01, + 1.50809372e-01, 1.62391604e-01, 1.74411338e-01, 1.86859813e-01, + 1.99726844e-01, 2.13000841e-01, 2.26668825e-01, 2.40716465e-01, + 2.55128110e-01, 2.69886836e-01, 2.84974488e-01, 3.00371741e-01, + 3.16058154e-01, 3.32012241e-01, 3.48211540e-01, 3.64632687e-01, + 3.81251499e-01, 3.98043054e-01, 4.14981787e-01, 4.32041576e-01, + 4.49195839e-01, 4.66417630e-01, 4.83679744e-01, 5.00954812e-01, + 5.18215407e-01, 5.35434150e-01, 5.52583807e-01, 5.69637402e-01, + 5.86568309e-01, 6.03350364e-01, 6.19957957e-01, 6.36366131e-01, + 6.52550680e-01, 6.68488237e-01, 6.84156362e-01, 6.99533624e-01, + 7.14599684e-01, 7.29335364e-01, 7.43722715e-01, 7.57745082e-01, + 7.71387158e-01, 7.84635031e-01, 7.97476230e-01, 8.09899758e-01, + 8.21896119e-01, 8.33457339e-01, 8.44576978e-01, 8.55250134e-01, + 8.65473441e-01, 8.75245054e-01, 8.84564635e-01, 8.93433321e-01, + 9.01853689e-01, 9.09829718e-01, 9.17366735e-01, 9.24471360e-01, + 9.31151439e-01, 9.37415982e-01, 9.43275077e-01, 9.48739819e-01, + 9.53822215e-01, 9.58535102e-01, 9.62892048e-01, 9.66907259e-01, + 9.70595479e-01, 9.73971891e-01, 9.77052016e-01, 9.79851615e-01, + 9.82386589e-01, 9.84672882e-01, 9.86726389e-01, 9.88562863e-01, + 9.90197830e-01, 9.91646510e-01, 9.92923740e-01, 9.94043905e-01, + 9.95020875e-01, 9.95867954e-01, 9.96597827e-01, 9.97222527e-01, + 9.97753399e-01, 9.98201077e-01, 9.98575471e-01, 9.98885756e-01, + 9.99140376e-01, 9.99347044e-01, 9.99512763e-01, 9.99643838e-01, + 9.99745907e-01, 9.99823966e-01, 9.99882403e-01, 9.99925037e-01, + 9.99955157e-01, 9.99975561e-01, 9.99988598e-01, 9.99996217e-01, + 9.99996217e-01, 9.99988598e-01, 9.99975561e-01, 9.99955157e-01, + 9.99925037e-01, 9.99882403e-01, 9.99823966e-01, 9.99745907e-01, + 9.99643838e-01, 9.99512763e-01, 9.99347044e-01, 9.99140376e-01, + 9.98885756e-01, 9.98575471e-01, 9.98201077e-01, 9.97753399e-01, + 9.97222527e-01, 9.96597827e-01, 9.95867954e-01, 9.95020875e-01, + 9.94043905e-01, 9.92923740e-01, 9.91646510e-01, 9.90197830e-01, + 9.88562863e-01, 9.86726389e-01, 9.84672882e-01, 9.82386589e-01, + 9.79851615e-01, 9.77052016e-01, 9.73971891e-01, 9.70595479e-01, + 9.66907259e-01, 9.62892048e-01, 9.58535102e-01, 9.53822215e-01, + 9.48739819e-01, 9.43275077e-01, 9.37415982e-01, 9.31151439e-01, + 9.24471360e-01, 9.17366735e-01, 9.09829718e-01, 9.01853689e-01, + 8.93433321e-01, 8.84564635e-01, 8.75245054e-01, 8.65473441e-01, + 8.55250134e-01, 8.44576978e-01, 8.33457339e-01, 8.21896119e-01, + 8.09899758e-01, 7.97476230e-01, 7.84635031e-01, 7.71387158e-01, + 7.57745082e-01, 7.43722715e-01, 7.29335364e-01, 7.14599684e-01, + 6.99533624e-01, 6.84156362e-01, 6.68488237e-01, 6.52550680e-01, + 6.36366131e-01, 6.19957957e-01, 6.03350364e-01, 5.86568309e-01, + 5.69637402e-01, 5.52583807e-01, 5.35434150e-01, 5.18215407e-01, + 5.00954812e-01, 4.83679744e-01, 4.66417630e-01, 4.49195839e-01, + 4.32041576e-01, 4.14981787e-01, 3.98043054e-01, 3.81251499e-01, + 3.64632687e-01, 3.48211540e-01, 3.32012241e-01, 3.16058154e-01, + 3.00371741e-01, 2.84974488e-01, 2.69886836e-01, 2.55128110e-01, + 2.40716465e-01, 2.26668825e-01, 2.13000841e-01, 1.99726844e-01, + 1.86859813e-01, 1.74411338e-01, 1.62391604e-01, 1.50809372e-01, + 1.39671964e-01, 1.28985265e-01, 1.18753719e-01, 1.08980342e-01, + 9.96667332e-02, 9.08130967e-02, 8.24182658e-02, 7.44797342e-02, + 6.69936920e-02, 5.99550649e-02, 5.33575588e-02, 4.71937052e-02, + 4.14549100e-02, 3.61315008e-02, 3.12127707e-02, 2.66870142e-02, + 2.25415434e-02, 1.87626677e-02, 1.53355947e-02, 1.22441576e-02, + 9.47011816e-03, 6.99126548e-03, 4.77524515e-03, 2.75074638e-03, +}; + +static const float mdct_win_5m_8k[__LC3_MDCT_WIN_LEN(5M, 8K)] = { + 9.95908659e-04, 3.81905679e-03, 9.54083261e-03, 1.92165980e-02, + 3.38271908e-02, 5.42483167e-02, 8.12077767e-02, 1.15217189e-01, + 1.56494233e-01, 2.04936342e-01, 2.60116658e-01, 3.21281416e-01, + 3.87347300e-01, 4.56949708e-01, 5.28519296e-01, 6.00352249e-01, + 6.70689638e-01, 7.37804446e-01, 8.00092531e-01, 8.56140918e-01, + 9.04827229e-01, 9.45368503e-01, 9.77350743e-01, 1.00080087e+00, + 1.01617159e+00, 1.02431525e+00, 1.02641543e+00, 1.02385837e+00, + 1.01813571e+00, 1.01079482e+00, 1.00340651e+00, 9.96783127e-01, + 9.92099552e-01, 9.89220694e-01, 9.87965832e-01, 9.88127353e-01, + 9.89480554e-01, 9.91784992e-01, 9.94784758e-01, 9.98211967e-01, + 1.00179124e+00, 1.00524258e+00, 1.00828305e+00, 1.01063128e+00, + 1.01201530e+00, 1.01218075e+00, 1.01089677e+00, 1.00796336e+00, + 1.00322726e+00, 9.96605055e-01, 9.86828423e-01, 9.73125029e-01, + 9.54063648e-01, 9.28386428e-01, 8.95091686e-01, 8.53476936e-01, + 8.03209093e-01, 7.44473520e-01, 6.78078703e-01, 6.05397045e-01, + 5.28207751e-01, 4.48655296e-01, 3.69187599e-01, 2.92456641e-01, + 2.21071854e-01, 1.57314858e-01, 1.03052576e-01, 5.98273224e-02, + 2.87183192e-02, 9.68388493e-03, +}; + +static const float mdct_win_5m_16k[__LC3_MDCT_WIN_LEN(5M, 16K)] = { + 6.14338818e-04, 1.48958283e-03, 2.88410496e-03, 4.93429883e-03, + 7.77913046e-03, 1.15491061e-02, 1.63715562e-02, 2.23711616e-02, + 2.96615969e-02, 3.83566333e-02, 4.85561099e-02, 6.03505574e-02, + 7.38228820e-02, 8.90356369e-02, 1.06035623e-01, 1.24853486e-01, + 1.45493189e-01, 1.67943556e-01, 1.92172862e-01, 2.18123826e-01, + 2.45725974e-01, 2.74883943e-01, 3.05482471e-01, 3.37387380e-01, + 3.70441593e-01, 4.04474963e-01, 4.39300436e-01, 4.74722545e-01, + 5.10534149e-01, 5.46520192e-01, 5.82465810e-01, 6.18145266e-01, + 6.53341146e-01, 6.87836730e-01, 7.21417603e-01, 7.53888797e-01, + 7.85054657e-01, 8.14739745e-01, 8.42781936e-01, 8.69037674e-01, + 8.93393548e-01, 9.15748356e-01, 9.36027020e-01, 9.54173114e-01, + 9.70163547e-01, 9.84003644e-01, 9.95719942e-01, 1.00537427e+00, + 1.01304666e+00, 1.01884338e+00, 1.02289695e+00, 1.02535529e+00, + 1.02638288e+00, 1.02615553e+00, 1.02485397e+00, 1.02266460e+00, + 1.01977940e+00, 1.01639169e+00, 1.01269703e+00, 1.00888519e+00, + 1.00537874e+00, 1.00156378e+00, 9.98253156e-01, 9.95434664e-01, + 9.93095027e-01, 9.91217091e-01, 9.89780519e-01, 9.88762494e-01, + 9.88138324e-01, 9.87881941e-01, 9.87966213e-01, 9.88363051e-01, + 9.89043407e-01, 9.89977232e-01, 9.91133456e-01, 9.92480044e-01, + 9.93984121e-01, 9.95612147e-01, 9.97330059e-01, 9.99103363e-01, + 1.00089744e+00, 1.00267709e+00, 1.00440719e+00, 1.00605229e+00, + 1.00757693e+00, 1.00894586e+00, 1.01012424e+00, 1.01107797e+00, + 1.01177396e+00, 1.01218036e+00, 1.01226671e+00, 1.01200406e+00, + 1.01136522e+00, 1.01032500e+00, 1.00886073e+00, 1.00695298e+00, + 1.00458627e+00, 1.00174990e+00, 9.98438663e-01, 9.94650033e-01, + 9.89575685e-01, 9.83830313e-01, 9.76999916e-01, 9.68914116e-01, + 9.59403812e-01, 9.48308632e-01, 9.35486022e-01, 9.20810131e-01, + 9.04173226e-01, 8.85488225e-01, 8.64686495e-01, 8.41723747e-01, + 8.16587571e-01, 7.89298635e-01, 7.59917189e-01, 7.28547452e-01, + 6.95328294e-01, 6.60433402e-01, 6.24066143e-01, 5.86446142e-01, + 5.47816066e-01, 5.08449976e-01, 4.68636143e-01, 4.28678989e-01, + 3.88903272e-01, 3.49643142e-01, 3.11236082e-01, 2.74012847e-01, + 2.38284723e-01, 2.04337983e-01, 1.72430586e-01, 1.42793979e-01, + 1.15638588e-01, 9.11582177e-02, 6.95274904e-02, 5.08897541e-02, + 3.53343019e-02, 2.28668041e-02, 1.33800502e-02, 6.64050653e-03, +}; + +static const float mdct_win_5m_24k[__LC3_MDCT_WIN_LEN(5M, 24K)] = { + 5.08722763e-04, 9.95908659e-04, 1.68220801e-03, 2.60969726e-03, + 3.81905679e-03, 5.34931959e-03, 7.24390638e-03, 9.54083261e-03, + 1.22763764e-02, 1.54895024e-02, 1.92165980e-02, 2.34936962e-02, + 2.83519958e-02, 3.38271908e-02, 3.99493954e-02, 4.67477524e-02, + 5.42483167e-02, 6.24777078e-02, 7.14583592e-02, 8.12077767e-02, + 9.17440041e-02, 1.03076496e-01, 1.15217189e-01, 1.28166571e-01, + 1.41926438e-01, 1.56494233e-01, 1.71859319e-01, 1.88013425e-01, + 2.04936342e-01, 2.22612306e-01, 2.41015124e-01, 2.60116658e-01, + 2.79887101e-01, 3.00288014e-01, 3.21281416e-01, 3.42820846e-01, + 3.64859656e-01, 3.87347300e-01, 4.10229495e-01, 4.33449453e-01, + 4.56949708e-01, 4.80669640e-01, 5.04547382e-01, 5.28519296e-01, + 5.52519610e-01, 5.76487245e-01, 6.00352249e-01, 6.24050987e-01, + 6.47518259e-01, 6.70689638e-01, 6.93502907e-01, 7.15892752e-01, + 7.37804446e-01, 7.59178724e-01, 7.79958661e-01, 8.00092531e-01, + 8.19531865e-01, 8.38228896e-01, 8.56140918e-01, 8.73231695e-01, + 8.89470202e-01, 9.04827229e-01, 9.19273638e-01, 9.32794041e-01, + 9.45368503e-01, 9.56988393e-01, 9.67648642e-01, 9.77350743e-01, + 9.86102783e-01, 9.93912241e-01, 1.00080087e+00, 1.00678781e+00, + 1.01190127e+00, 1.01617159e+00, 1.01963641e+00, 1.02233661e+00, + 1.02431525e+00, 1.02562130e+00, 1.02630344e+00, 1.02641543e+00, + 1.02600793e+00, 1.02513744e+00, 1.02385837e+00, 1.02222694e+00, + 1.02030055e+00, 1.01813571e+00, 1.01579215e+00, 1.01332597e+00, + 1.01079482e+00, 1.00826513e+00, 1.00604687e+00, 1.00340651e+00, + 1.00097740e+00, 9.98770454e-01, 9.96783127e-01, 9.95011891e-01, + 9.93452397e-01, 9.92099552e-01, 9.90947600e-01, 9.89990243e-01, + 9.89220694e-01, 9.88631804e-01, 9.88216090e-01, 9.87965832e-01, + 9.87873077e-01, 9.87929693e-01, 9.88127353e-01, 9.88457554e-01, + 9.88911587e-01, 9.89480554e-01, 9.90155346e-01, 9.90926656e-01, + 9.91784992e-01, 9.92720684e-01, 9.93723921e-01, 9.94784758e-01, + 9.95893149e-01, 9.97038957e-01, 9.98211967e-01, 9.99402034e-01, + 1.00059832e+00, 1.00179124e+00, 1.00296984e+00, 1.00412379e+00, + 1.00524258e+00, 1.00631572e+00, 1.00733269e+00, 1.00828305e+00, + 1.00915642e+00, 1.00994254e+00, 1.01063128e+00, 1.01121274e+00, + 1.01167723e+00, 1.01201530e+00, 1.01221778e+00, 1.01227579e+00, + 1.01218075e+00, 1.01192443e+00, 1.01149892e+00, 1.01089677e+00, + 1.01011097e+00, 1.00913509e+00, 1.00796336e+00, 1.00659076e+00, + 1.00501312e+00, 1.00322726e+00, 1.00123106e+00, 9.99023556e-01, + 9.96605055e-01, 9.93989471e-01, 9.90453920e-01, 9.86828423e-01, + 9.82771674e-01, 9.78220667e-01, 9.73125029e-01, 9.67432353e-01, + 9.61094704e-01, 9.54063648e-01, 9.46295299e-01, 9.37748911e-01, + 9.28386428e-01, 9.18176261e-01, 9.07086156e-01, 8.95091686e-01, + 8.82169624e-01, 8.68302529e-01, 8.53476936e-01, 8.37685201e-01, + 8.20927526e-01, 8.03209093e-01, 7.84545048e-01, 7.64955485e-01, + 7.44473520e-01, 7.23134807e-01, 7.00986056e-01, 6.78078703e-01, + 6.54468651e-01, 6.30221215e-01, 6.05397045e-01, 5.80071577e-01, + 5.54312928e-01, 5.28207751e-01, 5.01836972e-01, 4.75290296e-01, + 4.48655296e-01, 4.22028112e-01, 3.95505797e-01, 3.69187599e-01, + 3.43173285e-01, 3.17563302e-01, 2.92456641e-01, 2.67946378e-01, + 2.44123133e-01, 2.21071854e-01, 1.98871915e-01, 1.77596763e-01, + 1.57314858e-01, 1.38090336e-01, 1.19983750e-01, 1.03052576e-01, + 8.73508501e-02, 7.29281158e-02, 5.98273224e-02, 4.80817884e-02, + 3.77113530e-02, 2.87183192e-02, 2.10835203e-02, 1.47628941e-02, + 9.68388493e-03, 5.64216879e-03, +}; + +static const float mdct_win_5m_32k[__LC3_MDCT_WIN_LEN(5M, 32K)] = { + 4.59588635e-04, 7.91932361e-04, 1.22792717e-03, 1.78365327e-03, + 2.47954941e-03, 3.32979945e-03, 4.35353548e-03, 5.56496516e-03, + 6.98610836e-03, 8.62988232e-03, 1.05134341e-02, 1.26508264e-02, + 1.50609045e-02, 1.77559123e-02, 2.07547598e-02, 2.40681372e-02, + 2.77120786e-02, 3.16993325e-02, 3.60460964e-02, 4.07612864e-02, + 4.58603812e-02, 5.13513668e-02, 5.72478022e-02, 6.35585474e-02, + 7.02945073e-02, 7.74598720e-02, 8.50663537e-02, 9.31164162e-02, + 1.01616296e-01, 1.10569081e-01, 1.19978929e-01, 1.29841729e-01, + 1.40162380e-01, 1.50937156e-01, 1.62163230e-01, 1.73835412e-01, + 1.85952036e-01, 1.98500883e-01, 2.11477855e-01, 2.24873256e-01, + 2.38676395e-01, 2.52872945e-01, 2.67454701e-01, 2.82403147e-01, + 2.97705015e-01, 3.13341912e-01, 3.29297670e-01, 3.45549016e-01, + 3.62079505e-01, 3.78864867e-01, 3.95885158e-01, 4.13114379e-01, + 4.30530830e-01, 4.48107672e-01, 4.65822779e-01, 4.83646639e-01, + 5.01556485e-01, 5.19522807e-01, 5.37519704e-01, 5.55518384e-01, + 5.73495781e-01, 5.91418665e-01, 6.09262289e-01, 6.26998116e-01, + 6.44600201e-01, 6.62038458e-01, 6.79290655e-01, 6.96325643e-01, + 7.13119439e-01, 7.29646991e-01, 7.45886459e-01, 7.61809472e-01, + 7.77395845e-01, 7.92620875e-01, 8.07466639e-01, 8.21910156e-01, + 8.35934316e-01, 8.49518047e-01, 8.62648584e-01, 8.75308323e-01, + 8.87488472e-01, 8.99173772e-01, 9.10352743e-01, 9.21014413e-01, + 9.31155619e-01, 9.40764474e-01, 9.49838224e-01, 9.58373260e-01, + 9.66369041e-01, 9.73823562e-01, 9.80744251e-01, 9.87129797e-01, + 9.92987227e-01, 9.98324140e-01, 1.00315076e+00, 1.00747371e+00, + 1.01130915e+00, 1.01466668e+00, 1.01756334e+00, 1.02001468e+00, + 1.02203987e+00, 1.02365426e+00, 1.02488162e+00, 1.02573929e+00, + 1.02625071e+00, 1.02643667e+00, 1.02632086e+00, 1.02592292e+00, + 1.02526998e+00, 1.02438219e+00, 1.02328494e+00, 1.02200083e+00, + 1.02055597e+00, 1.01897139e+00, 1.01727518e+00, 1.01548913e+00, + 1.01363936e+00, 1.01174775e+00, 1.00984084e+00, 1.00793976e+00, + 1.00640740e+00, 1.00437483e+00, 1.00246981e+00, 1.00068907e+00, + 9.99034600e-01, 9.97502490e-01, 9.96094155e-01, 9.94805124e-01, + 9.93636273e-01, 9.92582654e-01, 9.91644701e-01, 9.90817076e-01, + 9.90099845e-01, 9.89487369e-01, 9.88979432e-01, 9.88570179e-01, + 9.88259191e-01, 9.88040442e-01, 9.87913369e-01, 9.87871810e-01, + 9.87915076e-01, 9.88036894e-01, 9.88236456e-01, 9.88507369e-01, + 9.88848709e-01, 9.89253949e-01, 9.89722041e-01, 9.90246329e-01, + 9.90825634e-01, 9.91453181e-01, 9.92127681e-01, 9.92842250e-01, + 9.93595510e-01, 9.94380481e-01, 9.95195724e-01, 9.96034188e-01, + 9.96894383e-01, 9.97769201e-01, 9.98657113e-01, 9.99550974e-01, + 1.00044923e+00, 1.00134469e+00, 1.00223579e+00, 1.00311529e+00, + 1.00398160e+00, 1.00482747e+00, 1.00565128e+00, 1.00644577e+00, + 1.00720935e+00, 1.00793478e+00, 1.00862050e+00, 1.00925931e+00, + 1.00984974e+00, 1.01038469e+00, 1.01086278e+00, 1.01127704e+00, + 1.01162625e+00, 1.01190357e+00, 1.01210795e+00, 1.01223276e+00, + 1.01227709e+00, 1.01223451e+00, 1.01210432e+00, 1.01188029e+00, + 1.01156197e+00, 1.01114337e+00, 1.01062432e+00, 1.00999915e+00, + 1.00926803e+00, 1.00842570e+00, 1.00747277e+00, 1.00640448e+00, + 1.00522200e+00, 1.00392116e+00, 1.00250376e+00, 1.00096633e+00, + 9.99311401e-01, 9.97536270e-01, 9.95644231e-01, 9.93633392e-01, + 9.90867748e-01, 9.88232633e-01, 9.85362057e-01, 9.82230509e-01, + 9.78818585e-01, 9.75102633e-01, 9.71063185e-01, 9.66675967e-01, + 9.61924219e-01, 9.56784199e-01, 9.51239430e-01, 9.45270024e-01, + 9.38861570e-01, 9.31994644e-01, 9.24659203e-01, 9.16838340e-01, + 9.08521803e-01, 8.99696701e-01, 8.90356205e-01, 8.80487793e-01, + 8.70088421e-01, 8.59149213e-01, 8.47668639e-01, 8.35642897e-01, + 8.23075389e-01, 8.09964930e-01, 7.96320451e-01, 7.82146054e-01, + 7.67454182e-01, 7.52256346e-01, 7.36570205e-01, 7.20409055e-01, + 7.03797511e-01, 6.86754281e-01, 6.69304189e-01, 6.51471096e-01, + 6.33285483e-01, 6.14768539e-01, 5.95955378e-01, 5.76873796e-01, + 5.57553429e-01, 5.38032014e-01, 5.18345403e-01, 4.98525942e-01, + 4.78615607e-01, 4.58647304e-01, 4.38664366e-01, 4.18704689e-01, + 3.98812306e-01, 3.79026292e-01, 3.59391483e-01, 3.39947413e-01, + 3.20739242e-01, 3.01806111e-01, 2.83190595e-01, 2.64928837e-01, + 2.47060855e-01, 2.29620112e-01, 2.12643372e-01, 1.96160182e-01, + 1.80203520e-01, 1.64799688e-01, 1.49978755e-01, 1.35764352e-01, + 1.22184253e-01, 1.09260199e-01, 9.70178845e-02, 8.54768028e-02, + 7.46597638e-02, 6.45825432e-02, 5.52628119e-02, 4.67097698e-02, + 3.89324443e-02, 3.19297601e-02, 2.56981064e-02, 2.02225927e-02, + 1.54831778e-02, 1.14492491e-02, 8.07648266e-03, 5.30004408e-03, +}; + +static const float mdct_win_5m_48k[__LC3_MDCT_WIN_LEN(5M, 48K)] = { + 4.09010650e-04, 6.14338818e-04, 8.57175988e-04, 1.14701506e-03, + 1.48958283e-03, 1.88977038e-03, 2.35300080e-03, 2.88410496e-03, + 3.48821379e-03, 4.17004043e-03, 4.93429883e-03, 5.78707651e-03, + 6.73381174e-03, 7.77913046e-03, 8.92704496e-03, 1.01820289e-02, + 1.15491061e-02, 1.30334922e-02, 1.46395129e-02, 1.63715562e-02, + 1.82345538e-02, 2.02330949e-02, 2.23711616e-02, 2.46523735e-02, + 2.70810194e-02, 2.96615969e-02, 3.23988485e-02, 3.52960177e-02, + 3.83566333e-02, 4.15844793e-02, 4.49832242e-02, 4.85561099e-02, + 5.23059648e-02, 5.62362458e-02, 6.03505574e-02, 6.46518632e-02, + 6.91419575e-02, 7.38228820e-02, 7.86970933e-02, 8.37676164e-02, + 8.90356369e-02, 9.45019924e-02, 1.00168019e-01, 1.06035623e-01, + 1.12106022e-01, 1.18378855e-01, 1.24853486e-01, 1.31530285e-01, + 1.38410308e-01, 1.45493189e-01, 1.52777295e-01, 1.60260884e-01, + 1.67943556e-01, 1.75824562e-01, 1.83902012e-01, 1.92172862e-01, + 2.00634430e-01, 2.09285388e-01, 2.18123826e-01, 2.27146226e-01, + 2.36347921e-01, 2.45725974e-01, 2.55277155e-01, 2.64998109e-01, + 2.74883943e-01, 2.84929644e-01, 2.95130651e-01, 3.05482471e-01, + 3.15979964e-01, 3.26616979e-01, 3.37387380e-01, 3.48285592e-01, + 3.59305769e-01, 3.70441593e-01, 3.81686239e-01, 3.93032978e-01, + 4.04474963e-01, 4.16005110e-01, 4.27615960e-01, 4.39300436e-01, + 4.51051633e-01, 4.62861605e-01, 4.74722545e-01, 4.86626671e-01, + 4.98566451e-01, 5.10534149e-01, 5.22521279e-01, 5.34519051e-01, + 5.46520192e-01, 5.58517277e-01, 5.70502154e-01, 5.82465810e-01, + 5.94399172e-01, 6.06294818e-01, 6.18145266e-01, 6.29942202e-01, + 6.41676874e-01, 6.53341146e-01, 6.64927754e-01, 6.76429270e-01, + 6.87836730e-01, 6.99142147e-01, 7.10337961e-01, 7.21417603e-01, + 7.32374610e-01, 7.43200803e-01, 7.53888797e-01, 7.64431550e-01, + 7.74822315e-01, 7.85054657e-01, 7.95122352e-01, 8.05019386e-01, + 8.14739745e-01, 8.24277441e-01, 8.33626711e-01, 8.42781936e-01, + 8.51738619e-01, 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9.95967277e-01, 9.94650033e-01, 9.93240400e-01, + 9.91251152e-01, 9.89575685e-01, 9.87771321e-01, 9.85857748e-01, + 9.83830313e-01, 9.81682263e-01, 9.79407449e-01, 9.76999916e-01, + 9.74452836e-01, 9.71759750e-01, 9.68914116e-01, 9.65910162e-01, + 9.62742183e-01, 9.59403812e-01, 9.55888998e-01, 9.52192243e-01, + 9.48308632e-01, 9.44233254e-01, 9.39960724e-01, 9.35486022e-01, + 9.30805297e-01, 9.25914697e-01, 9.20810131e-01, 9.15487360e-01, + 9.09942607e-01, 9.04173226e-01, 8.98176373e-01, 8.91949024e-01, + 8.85488225e-01, 8.78791944e-01, 8.71858584e-01, 8.64686495e-01, + 8.57273814e-01, 8.49619586e-01, 8.41723747e-01, 8.33586272e-01, + 8.25207443e-01, 8.16587571e-01, 8.07728037e-01, 7.98631159e-01, + 7.89298635e-01, 7.79733095e-01, 7.69937953e-01, 7.59917189e-01, + 7.49675842e-01, 7.39217684e-01, 7.28547452e-01, 7.17671448e-01, + 7.06596231e-01, 6.95328294e-01, 6.83873906e-01, 6.72239531e-01, + 6.60433402e-01, 6.48464360e-01, 6.36339500e-01, 6.24066143e-01, + 6.11653033e-01, 5.99109864e-01, 5.86446142e-01, 5.73669485e-01, + 5.60788103e-01, 5.47816066e-01, 5.34761979e-01, 5.21636515e-01, + 5.08449976e-01, 4.95213509e-01, 4.81938756e-01, 4.68636143e-01, + 4.55317077e-01, 4.41993995e-01, 4.28678989e-01, 4.15383779e-01, + 4.02121106e-01, 3.88903272e-01, 3.75742544e-01, 3.62651518e-01, + 3.49643142e-01, 3.36729082e-01, 3.23922808e-01, 3.11236082e-01, + 2.98680794e-01, 2.86269467e-01, 2.74012847e-01, 2.61922833e-01, + 2.50009844e-01, 2.38284723e-01, 2.26757849e-01, 2.15439100e-01, + 2.04337983e-01, 1.93463677e-01, 1.82825032e-01, 1.72430586e-01, + 1.62288635e-01, 1.52407188e-01, 1.42793979e-01, 1.33456585e-01, + 1.24402392e-01, 1.15638588e-01, 1.07172155e-01, 9.90098587e-02, + 9.11582177e-02, 8.36234486e-02, 7.64114037e-02, 6.95274904e-02, + 6.29765645e-02, 5.67628424e-02, 5.08897541e-02, 4.53598330e-02, + 4.01745731e-02, 3.53343019e-02, 3.08380606e-02, 2.66835542e-02, + 2.28668041e-02, 1.93823634e-02, 1.62231272e-02, 1.33800502e-02, + 1.08421860e-02, 8.59675398e-03, 6.64050653e-03, 5.17270311e-03, +}; + +#endif /* !LC3_NPLUS */ + +static const float mdct_win_7m5_8k[__LC3_MDCT_WIN_LEN(7M5, 8K)] = { + 2.95060859e-03, 7.17541132e-03, 1.37695374e-02, 2.30953556e-02, + 3.54036230e-02, 5.08289304e-02, 6.94696293e-02, 9.13884278e-02, + 1.16604575e-01, 1.45073546e-01, 1.76711174e-01, 2.11342953e-01, + 2.48768614e-01, 2.88701102e-01, 3.30823871e-01, 3.74814544e-01, + 4.20308013e-01, 4.66904918e-01, 5.14185341e-01, 5.61710041e-01, + 6.09026346e-01, 6.55671016e-01, 7.01218384e-01, 7.45240679e-01, + 7.87369206e-01, 8.27223833e-01, 8.64513675e-01, 8.98977415e-01, + 9.30407518e-01, 9.58599937e-01, 9.83447719e-01, 1.00488283e+00, + 1.02285381e+00, 1.03740495e+00, 1.04859791e+00, 1.05656184e+00, + 1.06149371e+00, 1.06362578e+00, 1.06325973e+00, 1.06074505e+00, + 1.05643590e+00, 1.05069500e+00, 1.04392435e+00, 1.03647725e+00, + 1.02872867e+00, 1.02106486e+00, 1.01400658e+00, 1.00727455e+00, + 1.00172250e+00, 9.97309592e-01, 9.93985158e-01, 9.91683335e-01, + 9.90325325e-01, 9.89822613e-01, 9.90074734e-01, 9.90975314e-01, + 9.92412851e-01, 9.94273149e-01, 9.96439157e-01, 9.98791616e-01, + 1.00120985e+00, 1.00357357e+00, 1.00575984e+00, 1.00764515e+00, + 1.00910687e+00, 1.01002476e+00, 1.01028203e+00, 1.00976919e+00, + 1.00838641e+00, 1.00605124e+00, 1.00269767e+00, 9.98280464e-01, + 9.92777987e-01, 9.86186892e-01, 9.77634164e-01, 9.67447270e-01, + 9.55129725e-01, 9.40389877e-01, 9.22959280e-01, 9.02607350e-01, + 8.79202689e-01, 8.52641750e-01, 8.22881272e-01, 7.89971715e-01, + 7.54030328e-01, 7.15255742e-01, 6.73936911e-01, 6.30414716e-01, + 5.85078858e-01, 5.38398518e-01, 4.90833753e-01, 4.42885823e-01, + 3.95091024e-01, 3.48004343e-01, 3.02196710e-01, 2.58227431e-01, + 2.16641416e-01, 1.77922122e-01, 1.42480547e-01, 1.10652194e-01, + 8.26995967e-02, 5.88334516e-02, 3.92030848e-02, 2.38629107e-02, + 1.26976223e-02, 5.35665361e-03, +}; + +static const float mdct_win_7m5_16k[__LC3_MDCT_WIN_LEN(7M5, 16K)] = { + 2.20824874e-03, 3.81014420e-03, 5.91552473e-03, 8.58361457e-03, + 1.18759723e-02, 1.58335301e-02, 2.04918652e-02, 2.58883593e-02, + 3.20415894e-02, 3.89616721e-02, 4.66742169e-02, 5.51849337e-02, + 6.45038384e-02, 7.46411071e-02, 8.56000162e-02, 9.73846703e-02, + 1.09993603e-01, 1.23419277e-01, 1.37655457e-01, 1.52690437e-01, + 1.68513363e-01, 1.85093105e-01, 2.02410419e-01, 2.20450365e-01, + 2.39167941e-01, 2.58526168e-01, 2.78498539e-01, 2.99038432e-01, + 3.20104862e-01, 3.41658622e-01, 3.63660034e-01, 3.86062695e-01, + 4.08815272e-01, 4.31871046e-01, 4.55176988e-01, 4.78676593e-01, + 5.02324813e-01, 5.26060916e-01, 5.49831283e-01, 5.73576883e-01, + 5.97241338e-01, 6.20770242e-01, 6.44099662e-01, 6.67176382e-01, + 6.89958854e-01, 7.12379980e-01, 7.34396372e-01, 7.55966688e-01, + 7.77036981e-01, 7.97558114e-01, 8.17490856e-01, 8.36796950e-01, + 8.55447310e-01, 8.73400798e-01, 8.90635719e-01, 9.07128770e-01, + 9.22848784e-01, 9.37763323e-01, 9.51860206e-01, 9.65130600e-01, + 9.77556541e-01, 9.89126209e-01, 9.99846919e-01, 1.00970073e+00, + 1.01868229e+00, 1.02681455e+00, 1.03408981e+00, 1.04051196e+00, + 1.04610837e+00, 1.05088565e+00, 1.05486289e+00, 1.05807221e+00, + 1.06053414e+00, 1.06227662e+00, 1.06333815e+00, 1.06375557e+00, + 1.06356632e+00, 1.06282156e+00, 1.06155996e+00, 1.05981709e+00, + 1.05765876e+00, 1.05512006e+00, 1.05223985e+00, 1.04908779e+00, + 1.04569860e+00, 1.04210831e+00, 1.03838099e+00, 1.03455276e+00, + 1.03067200e+00, 1.02679167e+00, 1.02295558e+00, 1.01920733e+00, + 1.01587289e+00, 1.01221017e+00, 1.00884559e+00, 1.00577851e+00, + 1.00300262e+00, 1.00051460e+00, 9.98309229e-01, 9.96378601e-01, + 9.94718132e-01, 9.93316216e-01, 9.92166957e-01, 9.91258603e-01, + 9.90581104e-01, 9.90123118e-01, 9.89873712e-01, 9.89818707e-01, + 9.89946800e-01, 9.90243175e-01, 9.90695564e-01, 9.91288540e-01, + 9.92009469e-01, 9.92842693e-01, 9.93775067e-01, 9.94790398e-01, + 9.95875534e-01, 9.97014367e-01, 9.98192871e-01, 9.99394506e-01, + 1.00060586e+00, 1.00181040e+00, 1.00299457e+00, 1.00414155e+00, + 1.00523688e+00, 1.00626393e+00, 1.00720890e+00, 1.00805489e+00, + 1.00878802e+00, 1.00939182e+00, 1.00985296e+00, 1.01015529e+00, + 1.01028602e+00, 1.01022988e+00, 1.00997541e+00, 1.00950846e+00, + 1.00881848e+00, 1.00789488e+00, 1.00672876e+00, 1.00530991e+00, + 1.00363456e+00, 1.00169363e+00, 9.99485663e-01, 9.97006370e-01, + 9.94254687e-01, 9.91231967e-01, 9.87937115e-01, 9.84375125e-01, + 9.79890963e-01, 9.75269879e-01, 9.70180498e-01, 9.64580027e-01, + 9.58425534e-01, 9.51684014e-01, 9.44320232e-01, 9.36290624e-01, + 9.27580507e-01, 9.18153414e-01, 9.07976524e-01, 8.97050058e-01, + 8.85351360e-01, 8.72857927e-01, 8.59579819e-01, 8.45502615e-01, + 8.30619943e-01, 8.14946648e-01, 7.98489378e-01, 7.81262450e-01, + 7.63291769e-01, 7.44590843e-01, 7.25199287e-01, 7.05153668e-01, + 6.84490545e-01, 6.63245210e-01, 6.41477162e-01, 6.19235334e-01, + 5.96559133e-01, 5.73519989e-01, 5.50173851e-01, 5.26568538e-01, + 5.02781159e-01, 4.78860889e-01, 4.54877894e-01, 4.30898123e-01, + 4.06993964e-01, 3.83234031e-01, 3.59680098e-01, 3.36408100e-01, + 3.13496418e-01, 2.91010565e-01, 2.69019585e-01, 2.47584348e-01, + 2.26788433e-01, 2.06677771e-01, 1.87310343e-01, 1.68739644e-01, + 1.51012382e-01, 1.34171842e-01, 1.18254662e-01, 1.03290734e-01, + 8.93117360e-02, 7.63429787e-02, 6.44077291e-02, 5.35243715e-02, + 4.37084453e-02, 3.49667099e-02, 2.72984629e-02, 2.06895808e-02, + 1.51125125e-02, 1.05228754e-02, 6.85547314e-03, 4.02351119e-03, +}; + +static const float mdct_win_7m5_24k[__LC3_MDCT_WIN_LEN(7M5, 24K)] = { + 1.97084908e-03, 2.95060859e-03, 4.12447721e-03, 5.52688664e-03, + 7.17541132e-03, 9.08757730e-03, 1.12819105e-02, 1.37695374e-02, + 1.65600266e-02, 1.96650895e-02, 2.30953556e-02, 2.68612894e-02, + 3.09632560e-02, 3.54036230e-02, 4.01915610e-02, 4.53331403e-02, + 5.08289304e-02, 5.66815448e-02, 6.28935304e-02, 6.94696293e-02, + 7.64106314e-02, 8.37160016e-02, 9.13884278e-02, 9.94294008e-02, + 1.07834725e-01, 1.16604575e-01, 1.25736503e-01, 1.35226811e-01, + 1.45073546e-01, 1.55273819e-01, 1.65822194e-01, 1.76711174e-01, + 1.87928776e-01, 1.99473180e-01, 2.11342953e-01, 2.23524554e-01, + 2.36003100e-01, 2.48768614e-01, 2.61813811e-01, 2.75129161e-01, + 2.88701102e-01, 3.02514034e-01, 3.16558805e-01, 3.30823871e-01, + 3.45295567e-01, 3.59963992e-01, 3.74814544e-01, 3.89831817e-01, + 4.05001010e-01, 4.20308013e-01, 4.35739515e-01, 4.51277817e-01, + 4.66904918e-01, 4.82609041e-01, 4.98375466e-01, 5.14185341e-01, + 5.30021478e-01, 5.45869352e-01, 5.61710041e-01, 5.77528151e-01, + 5.93304696e-01, 6.09026346e-01, 6.24674189e-01, 6.40227555e-01, + 6.55671016e-01, 6.70995935e-01, 6.86184559e-01, 7.01218384e-01, + 7.16078449e-01, 7.30756084e-01, 7.45240679e-01, 7.59515122e-01, + 7.73561955e-01, 7.87369206e-01, 8.00923138e-01, 8.14211386e-01, + 8.27223833e-01, 8.39952374e-01, 8.52386102e-01, 8.64513675e-01, + 8.76324079e-01, 8.87814288e-01, 8.98977415e-01, 9.09803319e-01, + 9.20284312e-01, 9.30407518e-01, 9.40169652e-01, 9.49567795e-01, + 9.58599937e-01, 9.67260260e-01, 9.75545166e-01, 9.83447719e-01, + 9.90971957e-01, 9.98119269e-01, 1.00488283e+00, 1.01125773e+00, + 1.01724436e+00, 1.02285381e+00, 1.02808734e+00, 1.03293706e+00, + 1.03740495e+00, 1.04150164e+00, 1.04523236e+00, 1.04859791e+00, + 1.05160340e+00, 1.05425505e+00, 1.05656184e+00, 1.05853400e+00, + 1.06017414e+00, 1.06149371e+00, 1.06249943e+00, 1.06320577e+00, + 1.06362578e+00, 1.06376487e+00, 1.06363778e+00, 1.06325973e+00, + 1.06264695e+00, 1.06180496e+00, 1.06074505e+00, 1.05948492e+00, + 1.05804533e+00, 1.05643590e+00, 1.05466218e+00, 1.05274047e+00, + 1.05069500e+00, 1.04853894e+00, 1.04627898e+00, 1.04392435e+00, + 1.04149540e+00, 1.03901003e+00, 1.03647725e+00, 1.03390793e+00, + 1.03131989e+00, 1.02872867e+00, 1.02614832e+00, 1.02358988e+00, + 1.02106486e+00, 1.01856262e+00, 1.01655770e+00, 1.01400658e+00, + 1.01162953e+00, 1.00938590e+00, 1.00727455e+00, 1.00529616e+00, + 1.00344526e+00, 1.00172250e+00, 1.00012792e+00, 9.98657533e-01, + 9.97309592e-01, 9.96083571e-01, 9.94976569e-01, 9.93985158e-01, + 9.93107530e-01, 9.92341305e-01, 9.91683335e-01, 9.91130070e-01, + 9.90678325e-01, 9.90325325e-01, 9.90067562e-01, 9.89901282e-01, + 9.89822613e-01, 9.89827845e-01, 9.89913241e-01, 9.90074734e-01, + 9.90308256e-01, 9.90609852e-01, 9.90975314e-01, 9.91400330e-01, + 9.91880966e-01, 9.92412851e-01, 9.92991779e-01, 9.93613381e-01, + 9.94273149e-01, 9.94966958e-01, 9.95690370e-01, 9.96439157e-01, + 9.97208572e-01, 9.97994275e-01, 9.98791616e-01, 9.99596062e-01, + 1.00040410e+00, 1.00120985e+00, 1.00200976e+00, 1.00279924e+00, + 1.00357357e+00, 1.00432828e+00, 1.00505850e+00, 1.00575984e+00, + 1.00642767e+00, 1.00705768e+00, 1.00764515e+00, 1.00818549e+00, + 1.00867427e+00, 1.00910687e+00, 1.00947916e+00, 1.00978659e+00, + 1.01002476e+00, 1.01018954e+00, 1.01027669e+00, 1.01028203e+00, + 1.01020174e+00, 1.01003208e+00, 1.00976919e+00, 1.00940939e+00, + 1.00894931e+00, 1.00838641e+00, 1.00771780e+00, 1.00694031e+00, + 1.00605124e+00, 1.00504879e+00, 1.00393183e+00, 1.00269767e+00, + 1.00134427e+00, 9.99872092e-01, 9.98280464e-01, 9.96566569e-01, + 9.94731737e-01, 9.92777987e-01, 9.90701374e-01, 9.88504165e-01, + 9.86186892e-01, 9.83711989e-01, 9.80584643e-01, 9.77634164e-01, + 9.74455033e-01, 9.71062916e-01, 9.67447270e-01, 9.63593926e-01, + 9.59491398e-01, 9.55129725e-01, 9.50501326e-01, 9.45592810e-01, + 9.40389877e-01, 9.34886760e-01, 9.29080559e-01, 9.22959280e-01, + 9.16509579e-01, 9.09724456e-01, 9.02607350e-01, 8.95155084e-01, + 8.87356154e-01, 8.79202689e-01, 8.70699698e-01, 8.61847424e-01, + 8.52641750e-01, 8.43077833e-01, 8.33154905e-01, 8.22881272e-01, + 8.12257597e-01, 8.01285439e-01, 7.89971715e-01, 7.78318177e-01, + 7.66337710e-01, 7.54030328e-01, 7.41407991e-01, 7.28477501e-01, + 7.15255742e-01, 7.01751739e-01, 6.87975632e-01, 6.73936911e-01, + 6.59652573e-01, 6.45139489e-01, 6.30414716e-01, 6.15483622e-01, + 6.00365852e-01, 5.85078858e-01, 5.69649536e-01, 5.54084810e-01, + 5.38398518e-01, 5.22614738e-01, 5.06756805e-01, 4.90833753e-01, + 4.74866033e-01, 4.58876566e-01, 4.42885823e-01, 4.26906539e-01, + 4.10970973e-01, 3.95091024e-01, 3.79291327e-01, 3.63587417e-01, + 3.48004343e-01, 3.32563201e-01, 3.17287485e-01, 3.02196710e-01, + 2.87309403e-01, 2.72643992e-01, 2.58227431e-01, 2.44072856e-01, + 2.30208977e-01, 2.16641416e-01, 2.03398481e-01, 1.90486162e-01, + 1.77922122e-01, 1.65726674e-01, 1.53906397e-01, 1.42480547e-01, + 1.31453980e-01, 1.20841778e-01, 1.10652194e-01, 1.00891734e-01, + 9.15718851e-02, 8.26995967e-02, 7.42815529e-02, 6.63242382e-02, + 5.88334516e-02, 5.18140676e-02, 4.52698346e-02, 3.92030848e-02, + 3.36144159e-02, 2.85023308e-02, 2.38629107e-02, 1.96894227e-02, + 1.59720527e-02, 1.26976223e-02, 9.84937739e-03, 7.40724463e-03, + 5.35665361e-03, 3.83226552e-03, +}; + +static const float mdct_win_7m5_32k[__LC3_MDCT_WIN_LEN(7M5, 32K)] = { + 1.84833037e-03, 2.56481839e-03, 3.36762118e-03, 4.28736617e-03, + 5.33830143e-03, 6.52679223e-03, 7.86112587e-03, 9.34628179e-03, + 1.09916868e-02, 1.28011172e-02, 1.47805911e-02, 1.69307043e-02, + 1.92592307e-02, 2.17696937e-02, 2.44685983e-02, 2.73556543e-02, + 3.04319230e-02, 3.36980464e-02, 3.71583577e-02, 4.08148180e-02, + 4.46708068e-02, 4.87262995e-02, 5.29820633e-02, 5.74382470e-02, + 6.20968580e-02, 6.69609767e-02, 7.20298364e-02, 7.73039146e-02, + 8.27825574e-02, 8.84682102e-02, 9.43607566e-02, 1.00460272e-01, + 1.06763824e-01, 1.13273679e-01, 1.19986420e-01, 1.26903521e-01, + 1.34020853e-01, 1.41339557e-01, 1.48857211e-01, 1.56573685e-01, + 1.64484622e-01, 1.72589077e-01, 1.80879090e-01, 1.89354320e-01, + 1.98012244e-01, 2.06854141e-01, 2.15875319e-01, 2.25068672e-01, + 2.34427407e-01, 2.43948314e-01, 2.53627993e-01, 2.63464061e-01, + 2.73450494e-01, 2.83582189e-01, 2.93853469e-01, 3.04257373e-01, + 3.14790914e-01, 3.25449123e-01, 3.36227410e-01, 3.47118760e-01, + 3.58120177e-01, 3.69224663e-01, 3.80427793e-01, 3.91720023e-01, + 4.03097022e-01, 4.14551955e-01, 4.26081719e-01, 4.37676318e-01, + 4.49330196e-01, 4.61034855e-01, 4.72786043e-01, 4.84576777e-01, + 4.96401707e-01, 5.08252458e-01, 5.20122078e-01, 5.32002077e-01, + 5.43888090e-01, 5.55771601e-01, 5.67645739e-01, 5.79502786e-01, + 5.91335035e-01, 6.03138367e-01, 6.14904172e-01, 6.26623941e-01, + 6.38288834e-01, 6.49893375e-01, 6.61432360e-01, 6.72902514e-01, + 6.84293750e-01, 6.95600460e-01, 7.06811784e-01, 7.17923425e-01, + 7.28931386e-01, 7.39832773e-01, 7.50618982e-01, 7.61284053e-01, + 7.71818919e-01, 7.82220992e-01, 7.92481330e-01, 8.02599448e-01, + 8.12565230e-01, 8.22377129e-01, 8.32030518e-01, 8.41523208e-01, + 8.50848313e-01, 8.60002412e-01, 8.68979881e-01, 8.77778347e-01, + 8.86395904e-01, 8.94829421e-01, 9.03077626e-01, 9.11132652e-01, + 9.18993585e-01, 9.26652937e-01, 9.34111420e-01, 9.41364344e-01, + 9.48412967e-01, 9.55255630e-01, 9.61892013e-01, 9.68316363e-01, + 9.74530156e-01, 9.80528338e-01, 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4.84849881e-01, 4.72868107e-01, + 4.60875918e-01, 4.48881081e-01, 4.36891039e-01, 4.24912022e-01, + 4.12960603e-01, 4.01035896e-01, 3.89157867e-01, 3.77322199e-01, + 3.65543767e-01, 3.53832356e-01, 3.42196115e-01, 3.30644820e-01, + 3.19187559e-01, 3.07833309e-01, 2.96588182e-01, 2.85463717e-01, + 2.74462409e-01, 2.63609584e-01, 2.52883101e-01, 2.42323489e-01, + 2.31925746e-01, 2.21690837e-01, 2.11638058e-01, 2.01766920e-01, + 1.92082236e-01, 1.82589160e-01, 1.73305997e-01, 1.64229200e-01, + 1.55362654e-01, 1.46717079e-01, 1.38299391e-01, 1.30105078e-01, + 1.22145310e-01, 1.14423458e-01, 1.06941076e-01, 9.97025893e-02, + 9.27124283e-02, 8.59737427e-02, 7.94893311e-02, 7.32616579e-02, + 6.72934102e-02, 6.15874081e-02, 5.61458003e-02, 5.09700747e-02, + 4.60617047e-02, 4.14220117e-02, 3.70514189e-02, 3.29494666e-02, + 2.91153327e-02, 2.55476401e-02, 2.22437711e-02, 1.92000659e-02, + 1.64122205e-02, 1.38747611e-02, 1.15806353e-02, 9.52213664e-03, + 7.69137380e-03, 6.07207833e-03, 4.62581217e-03, 3.60685164e-03, +}; + +static const float mdct_win_7m5_48k[__LC3_MDCT_WIN_LEN(7M5, 48K)] = { + 1.72152668e-03, 2.20824874e-03, 2.68901752e-03, 3.22613342e-03, + 3.81014420e-03, 4.45371932e-03, 5.15369240e-03, 5.91552473e-03, + 6.73869158e-03, 7.62861841e-03, 8.58361457e-03, 9.60938437e-03, + 1.07060753e-02, 1.18759723e-02, 1.31190130e-02, 1.44390108e-02, + 1.58335301e-02, 1.73063081e-02, 1.88584711e-02, 2.04918652e-02, + 2.22061476e-02, 2.40057166e-02, 2.58883593e-02, 2.78552326e-02, + 2.99059145e-02, 3.20415894e-02, 3.42610013e-02, 3.65680973e-02, + 3.89616721e-02, 4.14435824e-02, 4.40140796e-02, 4.66742169e-02, + 4.94214625e-02, 5.22588489e-02, 5.51849337e-02, 5.82005143e-02, + 6.13059845e-02, 6.45038384e-02, 6.77913923e-02, 7.11707833e-02, + 7.46411071e-02, 7.82028053e-02, 8.18549521e-02, 8.56000162e-02, + 8.94357617e-02, 9.33642589e-02, 9.73846703e-02, 1.01496718e-01, + 1.05698760e-01, 1.09993603e-01, 1.14378287e-01, 1.18853508e-01, + 1.23419277e-01, 1.28075997e-01, 1.32820581e-01, 1.37655457e-01, + 1.42578648e-01, 1.47590522e-01, 1.52690437e-01, 1.57878853e-01, + 1.63152529e-01, 1.68513363e-01, 1.73957969e-01, 1.79484737e-01, + 1.85093105e-01, 1.90784835e-01, 1.96556497e-01, 2.02410419e-01, + 2.08345433e-01, 2.14359825e-01, 2.20450365e-01, 2.26617296e-01, + 2.32856279e-01, 2.39167941e-01, 2.45550642e-01, 2.52003951e-01, + 2.58526168e-01, 2.65118408e-01, 2.71775911e-01, 2.78498539e-01, + 2.85284606e-01, 2.92132459e-01, 2.99038432e-01, 3.06004256e-01, + 3.13026529e-01, 3.20104862e-01, 3.27237324e-01, 3.34423210e-01, + 3.41658622e-01, 3.48944976e-01, 3.56279252e-01, 3.63660034e-01, + 3.71085146e-01, 3.78554327e-01, 3.86062695e-01, 3.93610554e-01, + 4.01195225e-01, 4.08815272e-01, 4.16468460e-01, 4.24155411e-01, + 4.31871046e-01, 4.39614744e-01, 4.47384019e-01, 4.55176988e-01, + 4.62990138e-01, 4.70824619e-01, 4.78676593e-01, 4.86545433e-01, + 4.94428714e-01, 5.02324813e-01, 5.10229471e-01, 5.18142927e-01, + 5.26060916e-01, 5.33982818e-01, 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1.23455562e-01, 1.18254662e-01, + 1.13159677e-01, 1.08171439e-01, 1.03290734e-01, 9.85202978e-02, + 9.38600023e-02, 8.93117360e-02, 8.48752103e-02, 8.05523737e-02, + 7.63429787e-02, 7.22489246e-02, 6.82699120e-02, 6.44077291e-02, + 6.06620003e-02, 5.70343711e-02, 5.35243715e-02, 5.01334690e-02, + 4.68610790e-02, 4.37084453e-02, 4.06748365e-02, 3.77612269e-02, + 3.49667099e-02, 3.22919275e-02, 2.97357669e-02, 2.72984629e-02, + 2.49787186e-02, 2.27762542e-02, 2.06895808e-02, 1.87178169e-02, + 1.68593418e-02, 1.51125125e-02, 1.34757094e-02, 1.19462709e-02, + 1.05228754e-02, 9.20130941e-03, 7.98124316e-03, 6.85547314e-03, + 5.82657334e-03, 4.87838525e-03, 4.02351119e-03, 3.15418663e-03, +}; + +static const float mdct_win_10m_8k[__LC3_MDCT_WIN_LEN(10M, 8K)] = { -7.07854671e-04, -2.09819773e-03, -4.52519808e-03, -8.23397633e-03, -1.33771310e-02, -1.99972156e-02, -2.80090946e-02, -3.72150208e-02, -4.73176826e-02, -5.79465483e-02, -6.86760675e-02, -7.90464744e-02, @@ -1031,7 +1991,7 @@ static const float mdct_win_10m_80[80+50] = { 2.70146141e-02, 9.99674359e-03, }; -static const float mdct_win_10m_160[160+100] = { +static const float mdct_win_10m_16k[__LC3_MDCT_WIN_LEN(10M, 16K)] = { -4.61989875e-04, -9.74716672e-04, -1.66447310e-03, -2.59710692e-03, -3.80628516e-03, -5.32460872e-03, -7.17588528e-03, -9.38248086e-03, -1.19527030e-02, -1.48952816e-02, -1.82066640e-02, -2.18757093e-02, @@ -1099,7 +2059,7 @@ static const float mdct_win_10m_160[160+100] = { 3.28807275e-02, 2.18305756e-02, 1.33638143e-02, 6.75812489e-03, }; -static const float mdct_win_10m_240[240+150] = { +static const float mdct_win_10m_24k[__LC3_MDCT_WIN_LEN(10M, 24K)] = { -3.61349642e-04, -7.07854671e-04, -1.07444364e-03, -1.53347854e-03, -2.09819773e-03, -2.77842087e-03, -3.58412992e-03, -4.52519808e-03, -5.60932724e-03, -6.84323454e-03, -8.23397633e-03, -9.78531476e-03, @@ -1200,7 +2160,7 @@ static const float mdct_win_10m_240[240+150] = { 9.99674359e-03, 5.30523510e-03, }; -static const float mdct_win_10m_320[320+200] = { +static const float mdct_win_10m_32k[__LC3_MDCT_WIN_LEN(10M, 32K)] = { -3.02115349e-04, -5.86773749e-04, -8.36650400e-04, -1.12663536e-03, -1.47049294e-03, -1.87347339e-03, -2.33929236e-03, -2.87200807e-03, -3.47625639e-03, -4.15596382e-03, -4.91456379e-03, -5.75517250e-03, @@ -1333,7 +2293,7 @@ static const float mdct_win_10m_320[320+200] = { 1.52571017e-02, 1.16378749e-02, 8.43308778e-03, 4.44966900e-03, }; -static const float mdct_win_10m_480[480+300] = { +static const float mdct_win_10m_48k[__LC3_MDCT_WIN_LEN(10M, 48K)] = { -2.35303215e-04, -4.61989875e-04, -6.26293154e-04, -7.92918043e-04, -9.74716672e-04, -1.18025689e-03, -1.40920904e-03, -1.66447310e-03, -1.94659161e-03, -2.25708173e-03, -2.59710692e-03, -2.96760762e-03, @@ -1531,572 +2491,320 @@ static const float mdct_win_10m_480[480+300] = { 1.10855888e-02, 8.94347419e-03, 6.75812489e-03, 3.50443813e-03, }; -static const float mdct_win_7m5_60[60+46] = { - 2.95060859e-03, 7.17541132e-03, 1.37695374e-02, 2.30953556e-02, - 3.54036230e-02, 5.08289304e-02, 6.94696293e-02, 9.13884278e-02, - 1.16604575e-01, 1.45073546e-01, 1.76711174e-01, 2.11342953e-01, - 2.48768614e-01, 2.88701102e-01, 3.30823871e-01, 3.74814544e-01, - 4.20308013e-01, 4.66904918e-01, 5.14185341e-01, 5.61710041e-01, - 6.09026346e-01, 6.55671016e-01, 7.01218384e-01, 7.45240679e-01, - 7.87369206e-01, 8.27223833e-01, 8.64513675e-01, 8.98977415e-01, - 9.30407518e-01, 9.58599937e-01, 9.83447719e-01, 1.00488283e+00, - 1.02285381e+00, 1.03740495e+00, 1.04859791e+00, 1.05656184e+00, - 1.06149371e+00, 1.06362578e+00, 1.06325973e+00, 1.06074505e+00, - 1.05643590e+00, 1.05069500e+00, 1.04392435e+00, 1.03647725e+00, - 1.02872867e+00, 1.02106486e+00, 1.01400658e+00, 1.00727455e+00, - 1.00172250e+00, 9.97309592e-01, 9.93985158e-01, 9.91683335e-01, - 9.90325325e-01, 9.89822613e-01, 9.90074734e-01, 9.90975314e-01, - 9.92412851e-01, 9.94273149e-01, 9.96439157e-01, 9.98791616e-01, - 1.00120985e+00, 1.00357357e+00, 1.00575984e+00, 1.00764515e+00, - 1.00910687e+00, 1.01002476e+00, 1.01028203e+00, 1.00976919e+00, - 1.00838641e+00, 1.00605124e+00, 1.00269767e+00, 9.98280464e-01, - 9.92777987e-01, 9.86186892e-01, 9.77634164e-01, 9.67447270e-01, - 9.55129725e-01, 9.40389877e-01, 9.22959280e-01, 9.02607350e-01, - 8.79202689e-01, 8.52641750e-01, 8.22881272e-01, 7.89971715e-01, - 7.54030328e-01, 7.15255742e-01, 6.73936911e-01, 6.30414716e-01, - 5.85078858e-01, 5.38398518e-01, 4.90833753e-01, 4.42885823e-01, - 3.95091024e-01, 3.48004343e-01, 3.02196710e-01, 2.58227431e-01, - 2.16641416e-01, 1.77922122e-01, 1.42480547e-01, 1.10652194e-01, - 8.26995967e-02, 5.88334516e-02, 3.92030848e-02, 2.38629107e-02, - 1.26976223e-02, 5.35665361e-03, -}; - -static const float mdct_win_7m5_120[120+92] = { - 2.20824874e-03, 3.81014420e-03, 5.91552473e-03, 8.58361457e-03, - 1.18759723e-02, 1.58335301e-02, 2.04918652e-02, 2.58883593e-02, - 3.20415894e-02, 3.89616721e-02, 4.66742169e-02, 5.51849337e-02, - 6.45038384e-02, 7.46411071e-02, 8.56000162e-02, 9.73846703e-02, - 1.09993603e-01, 1.23419277e-01, 1.37655457e-01, 1.52690437e-01, - 1.68513363e-01, 1.85093105e-01, 2.02410419e-01, 2.20450365e-01, - 2.39167941e-01, 2.58526168e-01, 2.78498539e-01, 2.99038432e-01, - 3.20104862e-01, 3.41658622e-01, 3.63660034e-01, 3.86062695e-01, - 4.08815272e-01, 4.31871046e-01, 4.55176988e-01, 4.78676593e-01, - 5.02324813e-01, 5.26060916e-01, 5.49831283e-01, 5.73576883e-01, - 5.97241338e-01, 6.20770242e-01, 6.44099662e-01, 6.67176382e-01, - 6.89958854e-01, 7.12379980e-01, 7.34396372e-01, 7.55966688e-01, - 7.77036981e-01, 7.97558114e-01, 8.17490856e-01, 8.36796950e-01, - 8.55447310e-01, 8.73400798e-01, 8.90635719e-01, 9.07128770e-01, - 9.22848784e-01, 9.37763323e-01, 9.51860206e-01, 9.65130600e-01, - 9.77556541e-01, 9.89126209e-01, 9.99846919e-01, 1.00970073e+00, - 1.01868229e+00, 1.02681455e+00, 1.03408981e+00, 1.04051196e+00, - 1.04610837e+00, 1.05088565e+00, 1.05486289e+00, 1.05807221e+00, - 1.06053414e+00, 1.06227662e+00, 1.06333815e+00, 1.06375557e+00, - 1.06356632e+00, 1.06282156e+00, 1.06155996e+00, 1.05981709e+00, - 1.05765876e+00, 1.05512006e+00, 1.05223985e+00, 1.04908779e+00, - 1.04569860e+00, 1.04210831e+00, 1.03838099e+00, 1.03455276e+00, - 1.03067200e+00, 1.02679167e+00, 1.02295558e+00, 1.01920733e+00, - 1.01587289e+00, 1.01221017e+00, 1.00884559e+00, 1.00577851e+00, - 1.00300262e+00, 1.00051460e+00, 9.98309229e-01, 9.96378601e-01, - 9.94718132e-01, 9.93316216e-01, 9.92166957e-01, 9.91258603e-01, - 9.90581104e-01, 9.90123118e-01, 9.89873712e-01, 9.89818707e-01, - 9.89946800e-01, 9.90243175e-01, 9.90695564e-01, 9.91288540e-01, - 9.92009469e-01, 9.92842693e-01, 9.93775067e-01, 9.94790398e-01, - 9.95875534e-01, 9.97014367e-01, 9.98192871e-01, 9.99394506e-01, - 1.00060586e+00, 1.00181040e+00, 1.00299457e+00, 1.00414155e+00, - 1.00523688e+00, 1.00626393e+00, 1.00720890e+00, 1.00805489e+00, - 1.00878802e+00, 1.00939182e+00, 1.00985296e+00, 1.01015529e+00, - 1.01028602e+00, 1.01022988e+00, 1.00997541e+00, 1.00950846e+00, - 1.00881848e+00, 1.00789488e+00, 1.00672876e+00, 1.00530991e+00, - 1.00363456e+00, 1.00169363e+00, 9.99485663e-01, 9.97006370e-01, - 9.94254687e-01, 9.91231967e-01, 9.87937115e-01, 9.84375125e-01, - 9.79890963e-01, 9.75269879e-01, 9.70180498e-01, 9.64580027e-01, - 9.58425534e-01, 9.51684014e-01, 9.44320232e-01, 9.36290624e-01, - 9.27580507e-01, 9.18153414e-01, 9.07976524e-01, 8.97050058e-01, - 8.85351360e-01, 8.72857927e-01, 8.59579819e-01, 8.45502615e-01, - 8.30619943e-01, 8.14946648e-01, 7.98489378e-01, 7.81262450e-01, - 7.63291769e-01, 7.44590843e-01, 7.25199287e-01, 7.05153668e-01, - 6.84490545e-01, 6.63245210e-01, 6.41477162e-01, 6.19235334e-01, - 5.96559133e-01, 5.73519989e-01, 5.50173851e-01, 5.26568538e-01, - 5.02781159e-01, 4.78860889e-01, 4.54877894e-01, 4.30898123e-01, - 4.06993964e-01, 3.83234031e-01, 3.59680098e-01, 3.36408100e-01, - 3.13496418e-01, 2.91010565e-01, 2.69019585e-01, 2.47584348e-01, - 2.26788433e-01, 2.06677771e-01, 1.87310343e-01, 1.68739644e-01, - 1.51012382e-01, 1.34171842e-01, 1.18254662e-01, 1.03290734e-01, - 8.93117360e-02, 7.63429787e-02, 6.44077291e-02, 5.35243715e-02, - 4.37084453e-02, 3.49667099e-02, 2.72984629e-02, 2.06895808e-02, - 1.51125125e-02, 1.05228754e-02, 6.85547314e-03, 4.02351119e-03, -}; - -static const float mdct_win_7m5_180[180+138] = { - 1.97084908e-03, 2.95060859e-03, 4.12447721e-03, 5.52688664e-03, - 7.17541132e-03, 9.08757730e-03, 1.12819105e-02, 1.37695374e-02, - 1.65600266e-02, 1.96650895e-02, 2.30953556e-02, 2.68612894e-02, - 3.09632560e-02, 3.54036230e-02, 4.01915610e-02, 4.53331403e-02, - 5.08289304e-02, 5.66815448e-02, 6.28935304e-02, 6.94696293e-02, - 7.64106314e-02, 8.37160016e-02, 9.13884278e-02, 9.94294008e-02, - 1.07834725e-01, 1.16604575e-01, 1.25736503e-01, 1.35226811e-01, - 1.45073546e-01, 1.55273819e-01, 1.65822194e-01, 1.76711174e-01, - 1.87928776e-01, 1.99473180e-01, 2.11342953e-01, 2.23524554e-01, - 2.36003100e-01, 2.48768614e-01, 2.61813811e-01, 2.75129161e-01, - 2.88701102e-01, 3.02514034e-01, 3.16558805e-01, 3.30823871e-01, - 3.45295567e-01, 3.59963992e-01, 3.74814544e-01, 3.89831817e-01, - 4.05001010e-01, 4.20308013e-01, 4.35739515e-01, 4.51277817e-01, - 4.66904918e-01, 4.82609041e-01, 4.98375466e-01, 5.14185341e-01, - 5.30021478e-01, 5.45869352e-01, 5.61710041e-01, 5.77528151e-01, - 5.93304696e-01, 6.09026346e-01, 6.24674189e-01, 6.40227555e-01, - 6.55671016e-01, 6.70995935e-01, 6.86184559e-01, 7.01218384e-01, - 7.16078449e-01, 7.30756084e-01, 7.45240679e-01, 7.59515122e-01, - 7.73561955e-01, 7.87369206e-01, 8.00923138e-01, 8.14211386e-01, - 8.27223833e-01, 8.39952374e-01, 8.52386102e-01, 8.64513675e-01, - 8.76324079e-01, 8.87814288e-01, 8.98977415e-01, 9.09803319e-01, - 9.20284312e-01, 9.30407518e-01, 9.40169652e-01, 9.49567795e-01, - 9.58599937e-01, 9.67260260e-01, 9.75545166e-01, 9.83447719e-01, - 9.90971957e-01, 9.98119269e-01, 1.00488283e+00, 1.01125773e+00, - 1.01724436e+00, 1.02285381e+00, 1.02808734e+00, 1.03293706e+00, - 1.03740495e+00, 1.04150164e+00, 1.04523236e+00, 1.04859791e+00, - 1.05160340e+00, 1.05425505e+00, 1.05656184e+00, 1.05853400e+00, - 1.06017414e+00, 1.06149371e+00, 1.06249943e+00, 1.06320577e+00, - 1.06362578e+00, 1.06376487e+00, 1.06363778e+00, 1.06325973e+00, - 1.06264695e+00, 1.06180496e+00, 1.06074505e+00, 1.05948492e+00, - 1.05804533e+00, 1.05643590e+00, 1.05466218e+00, 1.05274047e+00, - 1.05069500e+00, 1.04853894e+00, 1.04627898e+00, 1.04392435e+00, - 1.04149540e+00, 1.03901003e+00, 1.03647725e+00, 1.03390793e+00, - 1.03131989e+00, 1.02872867e+00, 1.02614832e+00, 1.02358988e+00, - 1.02106486e+00, 1.01856262e+00, 1.01655770e+00, 1.01400658e+00, - 1.01162953e+00, 1.00938590e+00, 1.00727455e+00, 1.00529616e+00, - 1.00344526e+00, 1.00172250e+00, 1.00012792e+00, 9.98657533e-01, - 9.97309592e-01, 9.96083571e-01, 9.94976569e-01, 9.93985158e-01, - 9.93107530e-01, 9.92341305e-01, 9.91683335e-01, 9.91130070e-01, - 9.90678325e-01, 9.90325325e-01, 9.90067562e-01, 9.89901282e-01, - 9.89822613e-01, 9.89827845e-01, 9.89913241e-01, 9.90074734e-01, - 9.90308256e-01, 9.90609852e-01, 9.90975314e-01, 9.91400330e-01, - 9.91880966e-01, 9.92412851e-01, 9.92991779e-01, 9.93613381e-01, - 9.94273149e-01, 9.94966958e-01, 9.95690370e-01, 9.96439157e-01, - 9.97208572e-01, 9.97994275e-01, 9.98791616e-01, 9.99596062e-01, - 1.00040410e+00, 1.00120985e+00, 1.00200976e+00, 1.00279924e+00, - 1.00357357e+00, 1.00432828e+00, 1.00505850e+00, 1.00575984e+00, - 1.00642767e+00, 1.00705768e+00, 1.00764515e+00, 1.00818549e+00, - 1.00867427e+00, 1.00910687e+00, 1.00947916e+00, 1.00978659e+00, - 1.01002476e+00, 1.01018954e+00, 1.01027669e+00, 1.01028203e+00, - 1.01020174e+00, 1.01003208e+00, 1.00976919e+00, 1.00940939e+00, - 1.00894931e+00, 1.00838641e+00, 1.00771780e+00, 1.00694031e+00, - 1.00605124e+00, 1.00504879e+00, 1.00393183e+00, 1.00269767e+00, - 1.00134427e+00, 9.99872092e-01, 9.98280464e-01, 9.96566569e-01, - 9.94731737e-01, 9.92777987e-01, 9.90701374e-01, 9.88504165e-01, - 9.86186892e-01, 9.83711989e-01, 9.80584643e-01, 9.77634164e-01, - 9.74455033e-01, 9.71062916e-01, 9.67447270e-01, 9.63593926e-01, - 9.59491398e-01, 9.55129725e-01, 9.50501326e-01, 9.45592810e-01, - 9.40389877e-01, 9.34886760e-01, 9.29080559e-01, 9.22959280e-01, - 9.16509579e-01, 9.09724456e-01, 9.02607350e-01, 8.95155084e-01, - 8.87356154e-01, 8.79202689e-01, 8.70699698e-01, 8.61847424e-01, - 8.52641750e-01, 8.43077833e-01, 8.33154905e-01, 8.22881272e-01, - 8.12257597e-01, 8.01285439e-01, 7.89971715e-01, 7.78318177e-01, - 7.66337710e-01, 7.54030328e-01, 7.41407991e-01, 7.28477501e-01, - 7.15255742e-01, 7.01751739e-01, 6.87975632e-01, 6.73936911e-01, - 6.59652573e-01, 6.45139489e-01, 6.30414716e-01, 6.15483622e-01, - 6.00365852e-01, 5.85078858e-01, 5.69649536e-01, 5.54084810e-01, - 5.38398518e-01, 5.22614738e-01, 5.06756805e-01, 4.90833753e-01, - 4.74866033e-01, 4.58876566e-01, 4.42885823e-01, 4.26906539e-01, - 4.10970973e-01, 3.95091024e-01, 3.79291327e-01, 3.63587417e-01, - 3.48004343e-01, 3.32563201e-01, 3.17287485e-01, 3.02196710e-01, - 2.87309403e-01, 2.72643992e-01, 2.58227431e-01, 2.44072856e-01, - 2.30208977e-01, 2.16641416e-01, 2.03398481e-01, 1.90486162e-01, - 1.77922122e-01, 1.65726674e-01, 1.53906397e-01, 1.42480547e-01, - 1.31453980e-01, 1.20841778e-01, 1.10652194e-01, 1.00891734e-01, - 9.15718851e-02, 8.26995967e-02, 7.42815529e-02, 6.63242382e-02, - 5.88334516e-02, 5.18140676e-02, 4.52698346e-02, 3.92030848e-02, - 3.36144159e-02, 2.85023308e-02, 2.38629107e-02, 1.96894227e-02, - 1.59720527e-02, 1.26976223e-02, 9.84937739e-03, 7.40724463e-03, - 5.35665361e-03, 3.83226552e-03, -}; - -static const float mdct_win_7m5_240[240+184] = { - 1.84833037e-03, 2.56481839e-03, 3.36762118e-03, 4.28736617e-03, - 5.33830143e-03, 6.52679223e-03, 7.86112587e-03, 9.34628179e-03, - 1.09916868e-02, 1.28011172e-02, 1.47805911e-02, 1.69307043e-02, - 1.92592307e-02, 2.17696937e-02, 2.44685983e-02, 2.73556543e-02, - 3.04319230e-02, 3.36980464e-02, 3.71583577e-02, 4.08148180e-02, - 4.46708068e-02, 4.87262995e-02, 5.29820633e-02, 5.74382470e-02, - 6.20968580e-02, 6.69609767e-02, 7.20298364e-02, 7.73039146e-02, - 8.27825574e-02, 8.84682102e-02, 9.43607566e-02, 1.00460272e-01, - 1.06763824e-01, 1.13273679e-01, 1.19986420e-01, 1.26903521e-01, - 1.34020853e-01, 1.41339557e-01, 1.48857211e-01, 1.56573685e-01, - 1.64484622e-01, 1.72589077e-01, 1.80879090e-01, 1.89354320e-01, - 1.98012244e-01, 2.06854141e-01, 2.15875319e-01, 2.25068672e-01, - 2.34427407e-01, 2.43948314e-01, 2.53627993e-01, 2.63464061e-01, - 2.73450494e-01, 2.83582189e-01, 2.93853469e-01, 3.04257373e-01, - 3.14790914e-01, 3.25449123e-01, 3.36227410e-01, 3.47118760e-01, - 3.58120177e-01, 3.69224663e-01, 3.80427793e-01, 3.91720023e-01, - 4.03097022e-01, 4.14551955e-01, 4.26081719e-01, 4.37676318e-01, - 4.49330196e-01, 4.61034855e-01, 4.72786043e-01, 4.84576777e-01, - 4.96401707e-01, 5.08252458e-01, 5.20122078e-01, 5.32002077e-01, - 5.43888090e-01, 5.55771601e-01, 5.67645739e-01, 5.79502786e-01, - 5.91335035e-01, 6.03138367e-01, 6.14904172e-01, 6.26623941e-01, - 6.38288834e-01, 6.49893375e-01, 6.61432360e-01, 6.72902514e-01, - 6.84293750e-01, 6.95600460e-01, 7.06811784e-01, 7.17923425e-01, - 7.28931386e-01, 7.39832773e-01, 7.50618982e-01, 7.61284053e-01, - 7.71818919e-01, 7.82220992e-01, 7.92481330e-01, 8.02599448e-01, - 8.12565230e-01, 8.22377129e-01, 8.32030518e-01, 8.41523208e-01, - 8.50848313e-01, 8.60002412e-01, 8.68979881e-01, 8.77778347e-01, - 8.86395904e-01, 8.94829421e-01, 9.03077626e-01, 9.11132652e-01, - 9.18993585e-01, 9.26652937e-01, 9.34111420e-01, 9.41364344e-01, - 9.48412967e-01, 9.55255630e-01, 9.61892013e-01, 9.68316363e-01, - 9.74530156e-01, 9.80528338e-01, 9.86313928e-01, 9.91886049e-01, - 9.97246345e-01, 1.00239190e+00, 1.00731946e+00, 1.01202707e+00, - 1.01651654e+00, 1.02079430e+00, 1.02486082e+00, 1.02871471e+00, - 1.03235170e+00, 1.03577375e+00, 1.03898432e+00, 1.04198786e+00, - 1.04478564e+00, 1.04737818e+00, 1.04976743e+00, 1.05195405e+00, - 1.05394290e+00, 1.05573463e+00, 1.05734177e+00, 1.05875726e+00, - 1.05998674e+00, 1.06103672e+00, 1.06190651e+00, 1.06260369e+00, - 1.06313289e+00, 1.06350237e+00, 1.06370981e+00, 1.06376322e+00, - 1.06366765e+00, 1.06343012e+00, 1.06305656e+00, 1.06255421e+00, - 1.06192235e+00, 1.06116702e+00, 1.06029469e+00, 1.05931469e+00, - 1.05823465e+00, 1.05705891e+00, 1.05578948e+00, 1.05442979e+00, - 1.05298793e+00, 1.05147505e+00, 1.04989930e+00, 1.04826213e+00, - 1.04656691e+00, 1.04481699e+00, 1.04302125e+00, 1.04118768e+00, - 1.03932339e+00, 1.03743168e+00, 1.03551757e+00, 1.03358511e+00, - 1.03164371e+00, 1.02969955e+00, 1.02775944e+00, 1.02582719e+00, - 1.02390791e+00, 1.02200805e+00, 1.02013910e+00, 1.01826310e+00, - 1.01687901e+00, 1.01492195e+00, 1.01309662e+00, 1.01134205e+00, - 1.00965912e+00, 1.00805036e+00, 1.00651754e+00, 1.00505799e+00, - 1.00366956e+00, 1.00235327e+00, 1.00110981e+00, 9.99937523e-01, - 9.98834524e-01, 9.97800606e-01, 9.96835756e-01, 9.95938881e-01, - 9.95108459e-01, 9.94343411e-01, 9.93642921e-01, 9.93005832e-01, - 9.92430984e-01, 9.91917493e-01, 9.91463898e-01, 9.91068214e-01, - 9.90729218e-01, 9.90446225e-01, 9.90217819e-01, 9.90041963e-01, - 9.89917085e-01, 9.89841975e-01, 9.89815048e-01, 9.89834329e-01, - 9.89898211e-01, 9.90005403e-01, 9.90154189e-01, 9.90342427e-01, - 9.90568459e-01, 9.90830953e-01, 9.91128038e-01, 9.91457566e-01, - 9.91817881e-01, 9.92207559e-01, 9.92624757e-01, 9.93067358e-01, - 9.93533398e-01, 9.94021410e-01, 9.94529685e-01, 9.95055964e-01, - 9.95598351e-01, 9.96155580e-01, 9.96725627e-01, 9.97306092e-01, - 9.97895214e-01, 9.98491441e-01, 9.99092890e-01, 9.99697063e-01, - 1.00030303e+00, 1.00090793e+00, 1.00151084e+00, 1.00210923e+00, - 1.00270118e+00, 1.00328513e+00, 1.00385926e+00, 1.00442111e+00, - 1.00496860e+00, 1.00550040e+00, 1.00601455e+00, 1.00650869e+00, - 1.00698104e+00, 1.00743004e+00, 1.00785364e+00, 1.00824962e+00, - 1.00861604e+00, 1.00895138e+00, 1.00925390e+00, 1.00952134e+00, - 1.00975175e+00, 1.00994371e+00, 1.01009550e+00, 1.01020488e+00, - 1.01027007e+00, 1.01028975e+00, 1.01026227e+00, 1.01018562e+00, - 1.01005820e+00, 1.00987882e+00, 1.00964593e+00, 1.00935753e+00, - 1.00901228e+00, 1.00860959e+00, 1.00814837e+00, 1.00762674e+00, - 1.00704343e+00, 1.00639775e+00, 1.00568877e+00, 1.00491559e+00, - 1.00407768e+00, 1.00317429e+00, 1.00220424e+00, 1.00116684e+00, - 1.00006248e+00, 9.98891422e-01, 9.97652252e-01, 9.96343856e-01, - 9.94967462e-01, 9.93524663e-01, 9.92013927e-01, 9.90433283e-01, - 9.88785147e-01, 9.87072681e-01, 9.85297443e-01, 9.83401161e-01, - 9.80949418e-01, 9.78782729e-01, 9.76468238e-01, 9.74042850e-01, - 9.71498848e-01, 9.68829968e-01, 9.66030974e-01, 9.63095104e-01, - 9.60018198e-01, 9.56795738e-01, 9.53426267e-01, 9.49903482e-01, - 9.46222115e-01, 9.42375820e-01, 9.38361702e-01, 9.34177798e-01, - 9.29823124e-01, 9.25292320e-01, 9.20580120e-01, 9.15679793e-01, - 9.10590604e-01, 9.05315030e-01, 8.99852756e-01, 8.94199497e-01, - 8.88350152e-01, 8.82301631e-01, 8.76054874e-01, 8.69612385e-01, - 8.62972799e-01, 8.56135198e-01, 8.49098179e-01, 8.41857024e-01, - 8.34414055e-01, 8.26774617e-01, 8.18939244e-01, 8.10904891e-01, - 8.02675318e-01, 7.94253751e-01, 7.85641662e-01, 7.76838609e-01, - 7.67853193e-01, 7.58685181e-01, 7.49330658e-01, 7.39809171e-01, - 7.30109944e-01, 7.20247781e-01, 7.10224161e-01, 7.00044326e-01, - 6.89711890e-01, 6.79231154e-01, 6.68608179e-01, 6.57850997e-01, - 6.46965718e-01, 6.35959617e-01, 6.24840336e-01, 6.13603503e-01, - 6.02265091e-01, 5.90829083e-01, 5.79309408e-01, 5.67711124e-01, - 5.56037416e-01, 5.44293664e-01, 5.32489768e-01, 5.20636084e-01, - 5.08743273e-01, 4.96811166e-01, 4.84849881e-01, 4.72868107e-01, - 4.60875918e-01, 4.48881081e-01, 4.36891039e-01, 4.24912022e-01, - 4.12960603e-01, 4.01035896e-01, 3.89157867e-01, 3.77322199e-01, - 3.65543767e-01, 3.53832356e-01, 3.42196115e-01, 3.30644820e-01, - 3.19187559e-01, 3.07833309e-01, 2.96588182e-01, 2.85463717e-01, - 2.74462409e-01, 2.63609584e-01, 2.52883101e-01, 2.42323489e-01, - 2.31925746e-01, 2.21690837e-01, 2.11638058e-01, 2.01766920e-01, - 1.92082236e-01, 1.82589160e-01, 1.73305997e-01, 1.64229200e-01, - 1.55362654e-01, 1.46717079e-01, 1.38299391e-01, 1.30105078e-01, - 1.22145310e-01, 1.14423458e-01, 1.06941076e-01, 9.97025893e-02, - 9.27124283e-02, 8.59737427e-02, 7.94893311e-02, 7.32616579e-02, - 6.72934102e-02, 6.15874081e-02, 5.61458003e-02, 5.09700747e-02, - 4.60617047e-02, 4.14220117e-02, 3.70514189e-02, 3.29494666e-02, - 2.91153327e-02, 2.55476401e-02, 2.22437711e-02, 1.92000659e-02, - 1.64122205e-02, 1.38747611e-02, 1.15806353e-02, 9.52213664e-03, - 7.69137380e-03, 6.07207833e-03, 4.62581217e-03, 3.60685164e-03, -}; - -static const float mdct_win_7m5_360[360+276] = { - 1.72152668e-03, 2.20824874e-03, 2.68901752e-03, 3.22613342e-03, - 3.81014420e-03, 4.45371932e-03, 5.15369240e-03, 5.91552473e-03, - 6.73869158e-03, 7.62861841e-03, 8.58361457e-03, 9.60938437e-03, - 1.07060753e-02, 1.18759723e-02, 1.31190130e-02, 1.44390108e-02, - 1.58335301e-02, 1.73063081e-02, 1.88584711e-02, 2.04918652e-02, - 2.22061476e-02, 2.40057166e-02, 2.58883593e-02, 2.78552326e-02, - 2.99059145e-02, 3.20415894e-02, 3.42610013e-02, 3.65680973e-02, - 3.89616721e-02, 4.14435824e-02, 4.40140796e-02, 4.66742169e-02, - 4.94214625e-02, 5.22588489e-02, 5.51849337e-02, 5.82005143e-02, - 6.13059845e-02, 6.45038384e-02, 6.77913923e-02, 7.11707833e-02, - 7.46411071e-02, 7.82028053e-02, 8.18549521e-02, 8.56000162e-02, - 8.94357617e-02, 9.33642589e-02, 9.73846703e-02, 1.01496718e-01, - 1.05698760e-01, 1.09993603e-01, 1.14378287e-01, 1.18853508e-01, - 1.23419277e-01, 1.28075997e-01, 1.32820581e-01, 1.37655457e-01, - 1.42578648e-01, 1.47590522e-01, 1.52690437e-01, 1.57878853e-01, - 1.63152529e-01, 1.68513363e-01, 1.73957969e-01, 1.79484737e-01, - 1.85093105e-01, 1.90784835e-01, 1.96556497e-01, 2.02410419e-01, - 2.08345433e-01, 2.14359825e-01, 2.20450365e-01, 2.26617296e-01, - 2.32856279e-01, 2.39167941e-01, 2.45550642e-01, 2.52003951e-01, - 2.58526168e-01, 2.65118408e-01, 2.71775911e-01, 2.78498539e-01, - 2.85284606e-01, 2.92132459e-01, 2.99038432e-01, 3.06004256e-01, - 3.13026529e-01, 3.20104862e-01, 3.27237324e-01, 3.34423210e-01, - 3.41658622e-01, 3.48944976e-01, 3.56279252e-01, 3.63660034e-01, - 3.71085146e-01, 3.78554327e-01, 3.86062695e-01, 3.93610554e-01, - 4.01195225e-01, 4.08815272e-01, 4.16468460e-01, 4.24155411e-01, - 4.31871046e-01, 4.39614744e-01, 4.47384019e-01, 4.55176988e-01, - 4.62990138e-01, 4.70824619e-01, 4.78676593e-01, 4.86545433e-01, - 4.94428714e-01, 5.02324813e-01, 5.10229471e-01, 5.18142927e-01, - 5.26060916e-01, 5.33982818e-01, 5.41906817e-01, 5.49831283e-01, - 5.57751234e-01, 5.65667636e-01, 5.73576883e-01, 5.81476666e-01, - 5.89364661e-01, 5.97241338e-01, 6.05102013e-01, 6.12946170e-01, - 6.20770242e-01, 6.28572094e-01, 6.36348526e-01, 6.44099662e-01, - 6.51820973e-01, 6.59513822e-01, 6.67176382e-01, 6.74806795e-01, - 6.82400711e-01, 6.89958854e-01, 6.97475722e-01, 7.04950145e-01, - 7.12379980e-01, 7.19765434e-01, 7.27103833e-01, 7.34396372e-01, - 7.41638561e-01, 7.48829639e-01, 7.55966688e-01, 7.63049259e-01, - 7.70072273e-01, 7.77036981e-01, 7.83941108e-01, 7.90781257e-01, - 7.97558114e-01, 8.04271381e-01, 8.10914901e-01, 8.17490856e-01, - 8.23997094e-01, 8.30432785e-01, 8.36796950e-01, 8.43089298e-01, - 8.49305847e-01, 8.55447310e-01, 8.61511037e-01, 8.67496281e-01, - 8.73400798e-01, 8.79227518e-01, 8.84972438e-01, 8.90635719e-01, - 8.96217173e-01, 9.01716414e-01, 9.07128770e-01, 9.12456578e-01, - 9.17697261e-01, 9.22848784e-01, 9.27909917e-01, 9.32882596e-01, - 9.37763323e-01, 9.42553356e-01, 9.47252428e-01, 9.51860206e-01, - 9.56376060e-01, 9.60800602e-01, 9.65130600e-01, 9.69366689e-01, - 9.73508812e-01, 9.77556541e-01, 9.81507226e-01, 9.85364580e-01, - 9.89126209e-01, 9.92794201e-01, 9.96367545e-01, 9.99846919e-01, - 1.00322812e+00, 1.00651341e+00, 1.00970073e+00, 1.01279029e+00, - 1.01578293e+00, 1.01868229e+00, 1.02148657e+00, 1.02419772e+00, - 1.02681455e+00, 1.02933598e+00, 1.03176043e+00, 1.03408981e+00, - 1.03632326e+00, 1.03846361e+00, 1.04051196e+00, 1.04246831e+00, - 1.04433331e+00, 1.04610837e+00, 1.04779018e+00, 1.04938334e+00, - 1.05088565e+00, 1.05229923e+00, 1.05362522e+00, 1.05486289e+00, - 1.05601521e+00, 1.05708746e+00, 1.05807221e+00, 1.05897524e+00, - 1.05979447e+00, 1.06053414e+00, 1.06119412e+00, 1.06177366e+00, - 1.06227662e+00, 1.06270324e+00, 1.06305569e+00, 1.06333815e+00, - 1.06354800e+00, 1.06368607e+00, 1.06375557e+00, 1.06375743e+00, - 1.06369358e+00, 1.06356632e+00, 1.06337707e+00, 1.06312782e+00, - 1.06282156e+00, 1.06245782e+00, 1.06203634e+00, 1.06155996e+00, - 1.06102951e+00, 1.06044797e+00, 1.05981709e+00, 1.05914163e+00, - 1.05842136e+00, 1.05765876e+00, 1.05685377e+00, 1.05600761e+00, - 1.05512006e+00, 1.05419505e+00, 1.05323346e+00, 1.05223985e+00, - 1.05121668e+00, 1.05016637e+00, 1.04908779e+00, 1.04798366e+00, - 1.04685334e+00, 1.04569860e+00, 1.04452056e+00, 1.04332348e+00, - 1.04210831e+00, 1.04087907e+00, 1.03963603e+00, 1.03838099e+00, - 1.03711403e+00, 1.03583813e+00, 1.03455276e+00, 1.03326200e+00, - 1.03196750e+00, 1.03067200e+00, 1.02937564e+00, 1.02808244e+00, - 1.02679167e+00, 1.02550635e+00, 1.02422655e+00, 1.02295558e+00, - 1.02169299e+00, 1.02044475e+00, 1.01920733e+00, 1.01799992e+00, - 1.01716022e+00, 1.01587289e+00, 1.01461783e+00, 1.01339738e+00, - 1.01221017e+00, 1.01105652e+00, 1.00993444e+00, 1.00884559e+00, - 1.00778956e+00, 1.00676790e+00, 1.00577851e+00, 1.00482173e+00, - 1.00389592e+00, 1.00300262e+00, 1.00214091e+00, 1.00131213e+00, - 1.00051460e+00, 9.99748988e-01, 9.99013486e-01, 9.98309229e-01, - 9.97634934e-01, 9.96991885e-01, 9.96378601e-01, 9.95795982e-01, - 9.95242217e-01, 9.94718132e-01, 9.94222122e-01, 9.93755313e-01, - 9.93316216e-01, 9.92905809e-01, 9.92522422e-01, 9.92166957e-01, - 9.91837704e-01, 9.91535508e-01, 9.91258603e-01, 9.91007878e-01, - 9.90781723e-01, 9.90581104e-01, 9.90404336e-01, 9.90252267e-01, - 9.90123118e-01, 9.90017726e-01, 9.89934325e-01, 9.89873712e-01, - 9.89834110e-01, 9.89816359e-01, 9.89818707e-01, 9.89841998e-01, - 9.89884438e-01, 9.89946800e-01, 9.90027287e-01, 9.90126680e-01, - 9.90243175e-01, 9.90377594e-01, 9.90528134e-01, 9.90695564e-01, - 9.90878043e-01, 9.91076302e-01, 9.91288540e-01, 9.91515602e-01, - 9.91755666e-01, 9.92009469e-01, 9.92275155e-01, 9.92553486e-01, - 9.92842693e-01, 9.93143533e-01, 9.93454080e-01, 9.93775067e-01, - 9.94104689e-01, 9.94443742e-01, 9.94790398e-01, 9.95145361e-01, - 9.95506800e-01, 9.95875534e-01, 9.96249681e-01, 9.96629919e-01, - 9.97014367e-01, 9.97403799e-01, 9.97796404e-01, 9.98192871e-01, - 9.98591286e-01, 9.98992436e-01, 9.99394506e-01, 9.99798247e-01, - 1.00020179e+00, 1.00060586e+00, 1.00100858e+00, 1.00141070e+00, - 1.00181040e+00, 1.00220846e+00, 1.00260296e+00, 1.00299457e+00, - 1.00338148e+00, 1.00376444e+00, 1.00414155e+00, 1.00451348e+00, - 1.00487832e+00, 1.00523688e+00, 1.00558730e+00, 1.00593027e+00, - 1.00626393e+00, 1.00658905e+00, 1.00690380e+00, 1.00720890e+00, - 1.00750238e+00, 1.00778498e+00, 1.00805489e+00, 1.00831287e+00, - 1.00855700e+00, 1.00878802e+00, 1.00900405e+00, 1.00920593e+00, - 1.00939182e+00, 1.00956244e+00, 1.00971590e+00, 1.00985296e+00, - 1.00997177e+00, 1.01007317e+00, 1.01015529e+00, 1.01021893e+00, - 1.01026225e+00, 1.01028602e+00, 1.01028842e+00, 1.01027030e+00, - 1.01022988e+00, 1.01016802e+00, 1.01008292e+00, 1.00997541e+00, - 1.00984369e+00, 1.00968863e+00, 1.00950846e+00, 1.00930404e+00, - 1.00907371e+00, 1.00881848e+00, 1.00853675e+00, 1.00822947e+00, - 1.00789488e+00, 1.00753391e+00, 1.00714488e+00, 1.00672876e+00, - 1.00628393e+00, 1.00581146e+00, 1.00530991e+00, 1.00478053e+00, - 1.00422177e+00, 1.00363456e+00, 1.00301719e+00, 1.00237067e+00, - 1.00169363e+00, 1.00098749e+00, 1.00025108e+00, 9.99485663e-01, - 9.98689592e-01, 9.97863666e-01, 9.97006370e-01, 9.96119199e-01, - 9.95201404e-01, 9.94254687e-01, 9.93277595e-01, 9.92270651e-01, - 9.91231967e-01, 9.90163286e-01, 9.89064394e-01, 9.87937115e-01, - 9.86779736e-01, 9.85592773e-01, 9.84375125e-01, 9.83129288e-01, - 9.81348463e-01, 9.79890963e-01, 9.78400459e-01, 9.76860435e-01, - 9.75269879e-01, 9.73627353e-01, 9.71931341e-01, 9.70180498e-01, - 9.68372652e-01, 9.66506952e-01, 9.64580027e-01, 9.62592318e-01, - 9.60540986e-01, 9.58425534e-01, 9.56244393e-01, 9.53998416e-01, - 9.51684014e-01, 9.49301185e-01, 9.46846884e-01, 9.44320232e-01, - 9.41718404e-01, 9.39042580e-01, 9.36290624e-01, 9.33464050e-01, - 9.30560854e-01, 9.27580507e-01, 9.24519592e-01, 9.21378471e-01, - 9.18153414e-01, 9.14844696e-01, 9.11451652e-01, 9.07976524e-01, - 9.04417545e-01, 9.00776308e-01, 8.97050058e-01, 8.93238398e-01, - 8.89338681e-01, 8.85351360e-01, 8.81274023e-01, 8.77109638e-01, - 8.72857927e-01, 8.68519505e-01, 8.64092796e-01, 8.59579819e-01, - 8.54976007e-01, 8.50285220e-01, 8.45502615e-01, 8.40630470e-01, - 8.35667925e-01, 8.30619943e-01, 8.25482007e-01, 8.20258909e-01, - 8.14946648e-01, 8.09546696e-01, 8.04059978e-01, 7.98489378e-01, - 7.92831417e-01, 7.87090668e-01, 7.81262450e-01, 7.75353947e-01, - 7.69363613e-01, 7.63291769e-01, 7.57139016e-01, 7.50901711e-01, - 7.44590843e-01, 7.38205136e-01, 7.31738075e-01, 7.25199287e-01, - 7.18588225e-01, 7.11905687e-01, 7.05153668e-01, 6.98332634e-01, - 6.91444101e-01, 6.84490545e-01, 6.77470119e-01, 6.70388375e-01, - 6.63245210e-01, 6.56045780e-01, 6.48788627e-01, 6.41477162e-01, - 6.34114323e-01, 6.26702000e-01, 6.19235334e-01, 6.11720596e-01, - 6.04161612e-01, 5.96559133e-01, 5.88914401e-01, 5.81234783e-01, - 5.73519989e-01, 5.65770616e-01, 5.57988067e-01, 5.50173851e-01, - 5.42330194e-01, 5.34460798e-01, 5.26568538e-01, 5.18656324e-01, - 5.10728813e-01, 5.02781159e-01, 4.94819491e-01, 4.86845139e-01, - 4.78860889e-01, 4.70869928e-01, 4.62875144e-01, 4.54877894e-01, - 4.46882512e-01, 4.38889325e-01, 4.30898123e-01, 4.22918322e-01, - 4.14950878e-01, 4.06993964e-01, 3.99052648e-01, 3.91134614e-01, - 3.83234031e-01, 3.75354653e-01, 3.67502060e-01, 3.59680098e-01, - 3.51887312e-01, 3.44130166e-01, 3.36408100e-01, 3.28728966e-01, - 3.21090505e-01, 3.13496418e-01, 3.05951565e-01, 2.98454319e-01, - 2.91010565e-01, 2.83621109e-01, 2.76285415e-01, 2.69019585e-01, - 2.61812445e-01, 2.54659232e-01, 2.47584348e-01, 2.40578694e-01, - 2.33647009e-01, 2.26788433e-01, 2.20001992e-01, 2.13301325e-01, - 2.06677771e-01, 2.00140409e-01, 1.93683630e-01, 1.87310343e-01, - 1.81027384e-01, 1.74839476e-01, 1.68739644e-01, 1.62737273e-01, - 1.56825277e-01, 1.51012382e-01, 1.45298230e-01, 1.39687469e-01, - 1.34171842e-01, 1.28762544e-01, 1.23455562e-01, 1.18254662e-01, - 1.13159677e-01, 1.08171439e-01, 1.03290734e-01, 9.85202978e-02, - 9.38600023e-02, 8.93117360e-02, 8.48752103e-02, 8.05523737e-02, - 7.63429787e-02, 7.22489246e-02, 6.82699120e-02, 6.44077291e-02, - 6.06620003e-02, 5.70343711e-02, 5.35243715e-02, 5.01334690e-02, - 4.68610790e-02, 4.37084453e-02, 4.06748365e-02, 3.77612269e-02, - 3.49667099e-02, 3.22919275e-02, 2.97357669e-02, 2.72984629e-02, - 2.49787186e-02, 2.27762542e-02, 2.06895808e-02, 1.87178169e-02, - 1.68593418e-02, 1.51125125e-02, 1.34757094e-02, 1.19462709e-02, - 1.05228754e-02, 9.20130941e-03, 7.98124316e-03, 6.85547314e-03, - 5.82657334e-03, 4.87838525e-03, 4.02351119e-03, 3.15418663e-03, -}; +#undef __LC3_MDCT_WIN_LEN const float *lc3_mdct_win[LC3_NUM_DT][LC3_NUM_SRATE] = { +#if !LC3_NPLUS + + [LC3_DT_2M5] = { + [LC3_SRATE_8K ] = mdct_win_2m5_8k, + [LC3_SRATE_16K] = mdct_win_2m5_16k, + [LC3_SRATE_24K] = mdct_win_2m5_24k, + [LC3_SRATE_32K] = mdct_win_2m5_32k, + [LC3_SRATE_48K] = mdct_win_2m5_48k, + }, + + [LC3_DT_5M] = { + [LC3_SRATE_8K ] = mdct_win_5m_8k, + [LC3_SRATE_16K] = mdct_win_5m_16k, + [LC3_SRATE_24K] = mdct_win_5m_24k, + [LC3_SRATE_32K] = mdct_win_5m_32k, + [LC3_SRATE_48K] = mdct_win_5m_48k, + }, + +#endif /* !LC3_NPLUS */ + [LC3_DT_7M5] = { - [LC3_SRATE_8K ] = mdct_win_7m5_60, - [LC3_SRATE_16K] = mdct_win_7m5_120, - [LC3_SRATE_24K] = mdct_win_7m5_180, - [LC3_SRATE_32K] = mdct_win_7m5_240, - [LC3_SRATE_48K] = mdct_win_7m5_360, + [LC3_SRATE_8K ] = mdct_win_7m5_8k, + [LC3_SRATE_16K] = mdct_win_7m5_16k, + [LC3_SRATE_24K] = mdct_win_7m5_24k, + [LC3_SRATE_32K] = mdct_win_7m5_32k, + [LC3_SRATE_48K] = mdct_win_7m5_48k, }, [LC3_DT_10M] = { - [LC3_SRATE_8K ] = mdct_win_10m_80, - [LC3_SRATE_16K] = mdct_win_10m_160, - [LC3_SRATE_24K] = mdct_win_10m_240, - [LC3_SRATE_32K] = mdct_win_10m_320, - [LC3_SRATE_48K] = mdct_win_10m_480, + [LC3_SRATE_8K ] = mdct_win_10m_8k, + [LC3_SRATE_16K] = mdct_win_10m_16k, + [LC3_SRATE_24K] = mdct_win_10m_24k, + [LC3_SRATE_32K] = mdct_win_10m_32k, + [LC3_SRATE_48K] = mdct_win_10m_48k, }, }; /** - * Bands limits (cf. 3.7.1-2) + * Bands limits */ -const int lc3_band_lim[LC3_NUM_DT][LC3_NUM_SRATE][LC3_NUM_BANDS+1] = { +#if !LC3_NPLUS + +static const uint16_t band_lim_2m5_8k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20 +}; + +static const uint16_t band_lim_2m5_16k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 32, 34, 36, 38, 40 +}; + +static const uint16_t band_lim_2m5_24k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 28, 30, 32, + 34, 36, 38, 40, 42, 44, 47, 50, 53, 56, + 60 +}; + +static const uint16_t band_lim_2m5_32k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 26, 28, 30, 32, 34, + 36, 38, 40, 43, 46, 49, 52, 55, 59, 63, + 67, 71, 75, 80 +}; + +static const uint16_t band_lim_2m5_48k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 23, 25, 27, 29, 31, 33, 35, 37, + 40, 43, 46, 49, 52, 56, 60, 64, 68, 72, + 77, 82, 87, 93, 100 +}; + +static const uint16_t band_lim_5m_8k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35, 36, 37, 38, 40 +}; + +static const uint16_t band_lim_5m_16k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, + 50, 52, 54, 57, 60, 63, 66, 69, 72, 76, + 80 +}; + +static const uint16_t band_lim_5m_24k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 26, 28, 30, 32, 34, + 36, 38, 40, 42, 44, 47, 50, 53, 56, 59, + 62, 65, 69, 73, 77, 81, 86, 91, 96, 101, + 107, 113, 120 +}; + +static const uint16_t band_lim_5m_32k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 24, 26, 28, 30, 32, 34, 36, + 38, 40, 42, 45, 48, 51, 54, 57, 61, 65, + 69, 73, 78, 83, 88, 93, 99, 105, 112, 119, + 126, 134, 142, 151, 160 +}; + +static const uint16_t band_lim_5m_48k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 23, 25, 27, 29, 31, 33, 35, 37, + 40, 43, 46, 49, 52, 55, 59, 63, 67, 72, + 77, 82, 87, 93, 99, 105, 112, 120, 128, 136, + 145, 155, 165, 176, 187, 200 +}; + +#endif /* !LC3_NPLUS */ + +static const uint16_t band_lim_7m5_8k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, + 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, + 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, + 60 +}; + +static const uint16_t band_lim_7m5_16k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 36, 38, 40, 42, 44, + 46, 48, 50, 52, 54, 56, 58, 60, 62, 65, + 68, 71, 74, 77, 80, 83, 86, 90, 94, 98, + 102, 106, 110, 115, 120 +}; + +static const uint16_t band_lim_7m5_24k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, + 33, 35, 37, 39, 41, 43, 45, 47, 49, 52, + 55, 58, 61, 64, 67, 70, 74, 78, 82, 86, + 90, 95, 100, 105, 110, 115, 121, 127, 134, 141, + 148, 155, 163, 171, 180 +}; + +static const uint16_t band_lim_7m5_32k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 26, 28, 30, 32, 34, + 36, 38, 40, 42, 45, 48, 51, 54, 57, 60, + 63, 67, 71, 75, 79, 84, 89, 94, 99, 105, + 111, 117, 124, 131, 138, 146, 154, 163, 172, 182, + 192, 203, 215, 227, 240 +}; + +static const uint16_t band_lim_7m5_48k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 24, 26, 28, 30, 32, 34, 36, + 38, 40, 43, 46, 49, 52, 55, 59, 63, 67, + 71, 75, 80, 85, 90, 96, 102, 108, 115, 122, + 129, 137, 146, 155, 165, 175, 186, 197, 209, 222, + 236, 251, 266, 283, 300 +}; + +static const uint16_t band_lim_10m_8k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, + 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, + 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, + 71, 73, 75, 77, 80 +}; + +static const uint16_t band_lim_10m_16k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, + 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, + 52, 55, 58, 61, 64, 67, 70, 73, 76, 80, + 84, 88, 92, 96, 101, 106, 111, 116, 121, 127, + 133, 139, 146, 153, 160 +}; + +static const uint16_t band_lim_10m_24k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 25, 27, 29, 31, 33, 35, + 37, 39, 41, 43, 46, 49, 52, 55, 58, 61, + 64, 68, 72, 76, 80, 85, 90, 95, 100, 106, + 112, 118, 125, 132, 139, 147, 155, 164, 173, 183, + 193, 204, 215, 227, 240 +}; + +static const uint16_t band_lim_10m_32k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, + 41, 44, 47, 50, 53, 56, 60, 64, 68, 72, + 76, 81, 86, 91, 97, 103, 109, 116, 123, 131, + 139, 148, 157, 166, 176, 187, 199, 211, 224, 238, + 252, 268, 284, 302, 320 +}; + +static const uint16_t band_lim_10m_48k[] = { + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, + 22, 24, 26, 28, 30, 32, 34, 36, 39, 42, + 45, 48, 51, 55, 59, 63, 67, 71, 76, 81, + 86, 92, 98, 105, 112, 119, 127, 135, 144, 154, + 164, 175, 186, 198, 211, 225, 240, 256, 273, 291, + 310, 330, 352, 375, 400 +}; + +const uint16_t *lc3_band_lim[LC3_NUM_DT][LC3_NUM_SRATE] = { + +#if !LC3_NPLUS + + [LC3_DT_2M5] = { + [LC3_SRATE_8K ] = band_lim_2m5_8k, + [LC3_SRATE_16K] = band_lim_2m5_16k, + [LC3_SRATE_24K] = band_lim_2m5_24k, + [LC3_SRATE_32K] = band_lim_2m5_32k, + [LC3_SRATE_48K] = band_lim_2m5_48k, + }, + + [LC3_DT_5M] = { + [LC3_SRATE_8K ] = band_lim_5m_8k, + [LC3_SRATE_16K] = band_lim_5m_16k, + [LC3_SRATE_24K] = band_lim_5m_24k, + [LC3_SRATE_32K] = band_lim_5m_32k, + [LC3_SRATE_48K] = band_lim_5m_48k, + }, + +#endif /* !LC3_NPLUS */ [LC3_DT_7M5] = { - - [LC3_SRATE_8K ] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, - 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, - 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, - 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, - 60, 60, 60, 60, 60 }, - - [LC3_SRATE_16K] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, - 30, 31, 32, 33, 34, 36, 38, 40, 42, 44, - 46, 48, 50, 52, 54, 56, 58, 60, 62, 65, - 68, 71, 74, 77, 80, 83, 86, 90, 94, 98, - 102, 106, 110, 115, 120 }, - - [LC3_SRATE_24K] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, - 33, 35, 37, 39, 41, 43, 45, 47, 49, 52, - 55, 58, 61, 64, 67, 70, 74, 78, 82, 86, - 90, 95, 100, 105, 110, 115, 121, 127, 134, 141, - 148, 155, 163, 171, 180 }, - - [LC3_SRATE_32K] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 26, 28, 30, 32, 34, - 36, 38, 40, 42, 45, 48, 51, 54, 57, 60, - 63, 67, 71, 75, 79, 84, 89, 94, 99, 105, - 111, 117, 124, 131, 138, 146, 154, 163, 172, 182, - 192, 203, 215, 227, 240 }, - - [LC3_SRATE_48K] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 24, 26, 28, 30, 32, 34, 36, - 38, 40, 43, 46, 49, 52, 55, 59, 63, 67, - 71, 75, 80, 85, 90, 96, 102, 108, 115, 122, - 129, 137, 146, 155, 165, 175, 186, 197, 209, 222, - 236, 251, 266, 283, 300 }, + [LC3_SRATE_8K ] = band_lim_7m5_8k, + [LC3_SRATE_16K] = band_lim_7m5_16k, + [LC3_SRATE_24K] = band_lim_7m5_24k, + [LC3_SRATE_32K] = band_lim_7m5_32k, + [LC3_SRATE_48K] = band_lim_7m5_48k, }, [LC3_DT_10M] = { + [LC3_SRATE_8K ] = band_lim_10m_8k, + [LC3_SRATE_16K] = band_lim_10m_16k, + [LC3_SRATE_24K] = band_lim_10m_24k, + [LC3_SRATE_32K] = band_lim_10m_32k, + [LC3_SRATE_48K] = band_lim_10m_48k, + }, +}; - [LC3_SRATE_8K ] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, - 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, - 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, - 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, - 71, 73, 75, 77, 80 }, +#define __LC3_NUM_BANDS(_lim) \ + ( sizeof(_lim) / sizeof(*(_lim)) - 1 ) - [LC3_SRATE_16K] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, - 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, - 52, 55, 58, 61, 64, 67, 70, 73, 76, 80, - 84, 88, 92, 96, 101, 106, 111, 116, 121, 127, - 133, 139, 146, 153, 160 }, +const int lc3_num_bands[LC3_NUM_DT][LC3_NUM_SRATE] = { - [LC3_SRATE_24K] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 21, 22, 23, 25, 27, 29, 31, 33, 35, - 37, 39, 41, 43, 46, 49, 52, 55, 58, 61, - 64, 68, 72, 76, 80, 85, 90, 95, 100, 106, - 112, 118, 125, 132, 139, 147, 155, 164, 173, 183, - 193, 204, 215, 227, 240 }, +#if !LC3_NPLUS - [LC3_SRATE_32K] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, - 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, - 41, 44, 47, 50, 53, 56, 60, 64, 68, 72, - 76, 81, 86, 91, 97, 103, 109, 116, 123, 131, - 139, 148, 157, 166, 176, 187, 199, 211, 224, 238, - 252, 268, 284, 302, 320 }, + [LC3_DT_2M5] = { + [LC3_SRATE_8K ] = __LC3_NUM_BANDS(band_lim_2m5_8k ), + [LC3_SRATE_16K] = __LC3_NUM_BANDS(band_lim_2m5_16k), + [LC3_SRATE_24K] = __LC3_NUM_BANDS(band_lim_2m5_24k), + [LC3_SRATE_32K] = __LC3_NUM_BANDS(band_lim_2m5_32k), + [LC3_SRATE_48K] = __LC3_NUM_BANDS(band_lim_2m5_48k), + }, - [LC3_SRATE_48K] = { - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, - 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, - 22, 24, 26, 28, 30, 32, 34, 36, 39, 42, - 45, 48, 51, 55, 59, 63, 67, 71, 76, 81, - 86, 92, 98, 105, 112, 119, 127, 135, 144, 154, - 164, 175, 186, 198, 211, 225, 240, 256, 273, 291, - 310, 330, 352, 375, 400 }, - } + [LC3_DT_5M] = { + [LC3_SRATE_8K ] = __LC3_NUM_BANDS(band_lim_5m_8k ), + [LC3_SRATE_16K] = __LC3_NUM_BANDS(band_lim_5m_16k), + [LC3_SRATE_24K] = __LC3_NUM_BANDS(band_lim_5m_24k), + [LC3_SRATE_32K] = __LC3_NUM_BANDS(band_lim_5m_32k), + [LC3_SRATE_48K] = __LC3_NUM_BANDS(band_lim_5m_48k), + }, + +#endif /* !LC3_NPLUS */ + + [LC3_DT_7M5] = { + [LC3_SRATE_8K ] = __LC3_NUM_BANDS(band_lim_7m5_8k ), + [LC3_SRATE_16K] = __LC3_NUM_BANDS(band_lim_7m5_16k), + [LC3_SRATE_24K] = __LC3_NUM_BANDS(band_lim_7m5_24k), + [LC3_SRATE_32K] = __LC3_NUM_BANDS(band_lim_7m5_32k), + [LC3_SRATE_48K] = __LC3_NUM_BANDS(band_lim_7m5_48k), + }, + + [LC3_DT_10M] = { + [LC3_SRATE_8K ] = __LC3_NUM_BANDS(band_lim_10m_8k ), + [LC3_SRATE_16K] = __LC3_NUM_BANDS(band_lim_10m_16k), + [LC3_SRATE_24K] = __LC3_NUM_BANDS(band_lim_10m_24k), + [LC3_SRATE_32K] = __LC3_NUM_BANDS(band_lim_10m_32k), + [LC3_SRATE_48K] = __LC3_NUM_BANDS(band_lim_10m_48k), + }, }; /** - * SNS Quantization (cf. 3.7.4) + * SNS Quantization */ const float lc3_sns_lfcb[32][8] = { @@ -2336,7 +3044,7 @@ const int32_t lc3_sns_mpvq_offsets[][11] = { /** - * TNS Arithmetic Coding (cf. 3.7.5) + * TNS Arithmetic Coding * The number of bits are given at 2048th of bits */ @@ -2442,7 +3150,7 @@ const uint16_t lc3_tns_coeffs_bits[][17] = { /** - * Long Term Postfilter Synthesis (cf. 3.7.6) + * Long Term Postfilter Synthesis * with - addition of a 0 for num coefficients * - remove of first 0 den coefficients */ @@ -2597,7 +3305,7 @@ const float *lc3_ltpf_cden[LC3_NUM_SRATE][4] = { /** - * Spectral Data Arithmetic Coding (cf. 3.7.7) + * Spectral Data Arithmetic Coding * The number of bits are given at 2048th of bits * * The dimensions of the lookup table are set as following : diff --git a/src/tables.h b/src/tables.h index 26bd48e..5377fd9 100644 --- a/src/tables.h +++ b/src/tables.h @@ -42,9 +42,10 @@ extern const float *lc3_mdct_win[LC3_NUM_DT][LC3_NUM_SRATE]; * Limits of bands */ -#define LC3_NUM_BANDS 64 +#define LC3_MAX_BANDS 64 -extern const int lc3_band_lim[LC3_NUM_DT][LC3_NUM_SRATE][LC3_NUM_BANDS+1]; +extern const int lc3_num_bands[LC3_NUM_DT][LC3_NUM_SRATE]; +extern const uint16_t *lc3_band_lim[LC3_NUM_DT][LC3_NUM_SRATE]; /** diff --git a/src/tns.c b/src/tns.c index 19bf149..7e80a5d 100644 --- a/src/tns.c +++ b/src/tns.c @@ -31,7 +31,7 @@ */ static bool resolve_lpc_weighting(enum lc3_dt dt, int nbytes) { - return nbytes < (dt == LC3_DT_7M5 ? 360/8 : 480/8); + return nbytes * 8 < 120 * (int)(1 + dt); } /** @@ -52,24 +52,42 @@ LC3_HOT static inline float dot(const float *a, const float *b, int n) /** * LPC Coefficients * dt, bw Duration and bandwidth of the frame + * maxorder Maximum order of filter * x Spectral coefficients * gain, a Output the prediction gains and LPC coefficients */ LC3_HOT static void compute_lpc_coeffs( - enum lc3_dt dt, enum lc3_bandwidth bw, + enum lc3_dt dt, enum lc3_bandwidth bw, int maxorder, const float *x, float *gain, float (*a)[9]) { - static const int sub_7m5_nb[] = { 9, 26, 43, 60 }; - static const int sub_7m5_wb[] = { 9, 46, 83, 120 }; - static const int sub_7m5_sswb[] = { 9, 66, 123, 180 }; - static const int sub_7m5_swb[] = { 9, 46, 82, 120, 159, 200, 240 }; - static const int sub_7m5_fb[] = { 9, 56, 103, 150, 200, 250, 300 }; - static const int sub_10m_nb[] = { 12, 34, 57, 80 }; - static const int sub_10m_wb[] = { 12, 61, 110, 160 }; - static const int sub_10m_sswb[] = { 12, 88, 164, 240 }; - static const int sub_10m_swb[] = { 12, 61, 110, 160, 213, 266, 320 }; - static const int sub_10m_fb[] = { 12, 74, 137, 200, 266, 333, 400 }; +#if !LC3_NPLUS + + static const int sub_2m5_nb[] = { 3, 10, 20 }; + static const int sub_2m5_wb[] = { 3, 20, 40 }; + static const int sub_2m5_sswb[] = { 3, 30, 60 }; + static const int sub_2m5_swb[] = { 3, 40, 80 }; + static const int sub_2m5_fb[] = { 3, 50, 100 }; + + static const int sub_5m_nb[] = { 6, 23, 40 }; + static const int sub_5m_wb[] = { 6, 43, 80 }; + static const int sub_5m_sswb[] = { 6, 63, 120 }; + static const int sub_5m_swb[] = { 6, 43, 80, 120, 160 }; + static const int sub_5m_fb[] = { 6, 53, 100, 150, 200 }; + +#endif /* !LC3_NPLUS */ + + static const int sub_7m5_nb[] = { 9, 26, 43, 60 }; + static const int sub_7m5_wb[] = { 9, 46, 83, 120 }; + static const int sub_7m5_sswb[] = { 9, 66, 123, 180 }; + static const int sub_7m5_swb[] = { 9, 46, 82, 120, 159, 200, 240 }; + static const int sub_7m5_fb[] = { 9, 56, 103, 150, 200, 250, 300 }; + + static const int sub_10m_nb[] = { 12, 34, 57, 80 }; + static const int sub_10m_wb[] = { 12, 61, 110, 160 }; + static const int sub_10m_sswb[] = { 12, 88, 164, 240 }; + static const int sub_10m_swb[] = { 12, 61, 110, 160, 213, 266, 320 }; + static const int sub_10m_fb[] = { 12, 74, 137, 200, 266, 333, 400 }; /* --- Normalized autocorrelation --- */ @@ -79,32 +97,53 @@ LC3_HOT static void compute_lpc_coeffs( 8.81323137e-01 }; - const int *sub = (const int * const [LC3_NUM_DT][LC3_NUM_SRATE]){ - { sub_7m5_nb, sub_7m5_wb, sub_7m5_sswb, sub_7m5_swb, sub_7m5_fb }, - { sub_10m_nb, sub_10m_wb, sub_10m_sswb, sub_10m_swb, sub_10m_fb }, + const int *sub = (const int * const [LC3_NUM_DT][LC3_NUM_BANDWIDTH]){ + +#if !LC3_NPLUS + + [LC3_DT_2M5] = { sub_2m5_nb, sub_2m5_wb, + sub_2m5_sswb, sub_2m5_swb, sub_2m5_fb }, + [LC3_DT_5M ] = { sub_5m_nb , sub_5m_wb , + sub_5m_sswb , sub_5m_swb , sub_5m_fb }, + +#endif /* !LC3_NPLUS */ + + [LC3_DT_7M5] = { sub_7m5_nb, sub_7m5_wb, + sub_7m5_sswb, sub_7m5_swb, sub_7m5_fb }, + [LC3_DT_10M] = { sub_10m_nb, sub_10m_wb, + sub_10m_sswb, sub_10m_swb, sub_10m_fb }, + }[dt][bw]; - int nfilters = 1 + (bw >= LC3_BANDWIDTH_SWB); + int nfilters = 1 + (dt >= LC3_DT_5M && bw >= LC3_BANDWIDTH_SWB); + int nsubdivisions = 2 + (dt >= LC3_DT_7M5); const float *xs, *xe = x + *sub; float r[2][9]; for (int f = 0; f < nfilters; f++) { - float c[9][3]; + float c[9][3] = { 0 }; - for (int s = 0; s < 3; s++) { + for (int s = 0; s < nsubdivisions; s++) { xs = xe, xe = x + *(++sub); - for (int k = 0; k < 9; k++) + for (int k = 0; k <= maxorder; k++) c[k][s] = dot(xs, xs + k, (xe - xs) - k); } - float e0 = c[0][0], e1 = c[0][1], e2 = c[0][2]; + r[f][0] = nsubdivisions; + if (nsubdivisions == 2) { + float e0 = c[0][0], e1 = c[0][1]; + for (int k = 1; k <= maxorder; k++) + r[f][k] = e0 == 0 || e1 == 0 ? 0 : + (c[k][0]/e0 + c[k][1]/e1) * lag_window[k]; - r[f][0] = 3; - for (int k = 1; k < 9; k++) - r[f][k] = e0 == 0 || e1 == 0 || e2 == 0 ? 0 : - (c[k][0]/e0 + c[k][1]/e1 + c[k][2]/e2) * lag_window[k]; + } else { + float e0 = c[0][0], e1 = c[0][1], e2 = c[0][2]; + for (int k = 1; k <= maxorder; k++) + r[f][k] = e0 == 0 || e1 == 0 || e2 == 0 ? 0 : + (c[k][0]/e0 + c[k][1]/e1 + c[k][2]/e2) * lag_window[k]; + } } /* --- Levinson-Durbin recursion --- */ @@ -116,7 +155,7 @@ LC3_HOT static void compute_lpc_coeffs( gain[f] = err; a0[0] = 1; - for (int k = 1; k < 9; ) { + for (int k = 1; k <= maxorder; ) { rc = -r[f][k]; for (int i = 1; i < k; i++) @@ -160,21 +199,22 @@ LC3_HOT static void lpc_weighting(float pred_gain, float *a) /** * LPC reflection - * a LPC coefficients + * a, maxorder LPC coefficients, and maximum order (4 or 8) * rc Output refelection coefficients */ -LC3_HOT static void lpc_reflection(const float *a, float *rc) +LC3_HOT static void lpc_reflection( + const float *a, int maxorder, float *rc) { float e, b[2][7], *b0, *b1; - rc[7] = a[1+7]; - e = 1 - rc[7] * rc[7]; + rc[maxorder-1] = a[maxorder]; + e = 1 - rc[maxorder-1] * rc[maxorder-1]; b1 = b[1]; - for (int i = 0; i < 7; i++) - b1[i] = (a[1+i] - rc[7] * a[7-i]) / e; + for (int i = 0; i < maxorder-1; i++) + b1[i] = (a[1+i] - rc[maxorder-1] * a[(maxorder-1)-i]) / e; - for (int k = 6; k > 0; k--) { + for (int k = maxorder-2; k > 0; k--) { b0 = b1, b1 = b[k & 1]; rc[k] = b0[k]; @@ -189,11 +229,11 @@ LC3_HOT static void lpc_reflection(const float *a, float *rc) /** * Quantization of RC coefficients - * rc Refelection coefficients - * rc_order Return order of coefficients + * rc, maxorder Refelection coefficients, and maximum order (4 or 8) + * order Return order of coefficients * rc_i Return quantized coefficients */ -static void quantize_rc(const float *rc, int *rc_order, int *rc_q) +static void quantize_rc(const float *rc, int maxorder, int *order, int *rc_q) { /* Quantization table, sin(delta * (i + 0.5)), delta = Pi / 17 */ @@ -202,9 +242,9 @@ static void quantize_rc(const float *rc, int *rc_order, int *rc_q) 7.39008917e-01, 8.50217136e-01, 9.32472229e-01, 9.82973100e-01 }; - *rc_order = 8; + *order = maxorder; - for (int i = 0; i < 8; i++) { + for (int i = 0; i < maxorder; i++) { float rc_m = fabsf(rc[i]); rc_q[i] = 4 * (rc_m >= q_thr[4]); @@ -213,17 +253,16 @@ static void quantize_rc(const float *rc, int *rc_order, int *rc_q) if (rc[i] < 0) rc_q[i] = -rc_q[i]; - *rc_order = rc_q[i] != 0 ? 8 : *rc_order - 1; + *order = rc_q[i] != 0 ? maxorder : *order - 1; } } /** * Unquantization of RC coefficients - * rc_q Quantized coefficients - * rc_order Order of coefficients + * rc_q, order Quantized coefficients, and order * rc Return refelection coefficients */ -static void unquantize_rc(const int *rc_q, int rc_order, float rc[8]) +static void unquantize_rc(const int *rc_q, int order, float rc[8]) { /* Quantization table, sin(delta * i), delta = Pi / 17 */ @@ -235,7 +274,7 @@ static void unquantize_rc(const int *rc_q, int rc_order, float rc[8]) int i; - for (i = 0; i < rc_order; i++) { + for (i = 0; i < order; i++) { float rc_m = q_inv[LC3_ABS(rc_q[i])]; rc[i] = rc_q[i] < 0 ? -rc_m : rc_m; } @@ -256,9 +295,9 @@ LC3_HOT static void forward_filtering( enum lc3_dt dt, enum lc3_bandwidth bw, const int rc_order[2], float (* const rc)[8], float *x) { - int nfilters = 1 + (bw >= LC3_BANDWIDTH_SWB); + int nfilters = 1 + (dt >= LC3_DT_5M && bw >= LC3_BANDWIDTH_SWB); int nf = LC3_NE(dt, bw) >> (nfilters - 1); - int i0, ie = 3*(3 + dt); + int i0, ie = 3*(1 + dt); float s[8] = { 0 }; @@ -297,9 +336,9 @@ LC3_HOT static void inverse_filtering( enum lc3_dt dt, enum lc3_bandwidth bw, const int rc_order[2], float (* const rc)[8], float *x) { - int nfilters = 1 + (bw >= LC3_BANDWIDTH_SWB); + int nfilters = 1 + (dt >= LC3_DT_5M && bw >= LC3_BANDWIDTH_SWB); int nf = LC3_NE(dt, bw) >> (nfilters - 1); - int i0, ie = 3*(3 + dt); + int i0, ie = 3*(1 + dt); float s[8] = { 0 }; @@ -349,10 +388,11 @@ void lc3_tns_analyze(enum lc3_dt dt, enum lc3_bandwidth bw, float pred_gain[2], a[2][9]; float rc[2][8]; - data->nfilters = 1 + (bw >= LC3_BANDWIDTH_SWB); data->lpc_weighting = resolve_lpc_weighting(dt, nbytes); + data->nfilters = 1 + (dt >= LC3_DT_5M && bw >= LC3_BANDWIDTH_SWB); + int maxorder = dt <= LC3_DT_5M ? 4 : 8; - compute_lpc_coeffs(dt, bw, x, pred_gain, a); + compute_lpc_coeffs(dt, bw, maxorder, x, pred_gain, a); for (int f = 0; f < data->nfilters; f++) { @@ -363,9 +403,9 @@ void lc3_tns_analyze(enum lc3_dt dt, enum lc3_bandwidth bw, if (data->lpc_weighting && pred_gain[f] < 2.f) lpc_weighting(pred_gain[f], a[f]); - lpc_reflection(a[f], rc[f]); + lpc_reflection(a[f], maxorder, rc[f]); - quantize_rc(rc[f], &data->rc_order[f], data->rc[f]); + quantize_rc(rc[f], maxorder, &data->rc_order[f], data->rc[f]); unquantize_rc(data->rc[f], data->rc_order[f], rc[f]); } @@ -435,10 +475,10 @@ void lc3_tns_put_data(lc3_bits_t *bits, const struct lc3_tns_data *data) /** * Get bitstream data */ -void lc3_tns_get_data(lc3_bits_t *bits, +int lc3_tns_get_data(lc3_bits_t *bits, enum lc3_dt dt, enum lc3_bandwidth bw, int nbytes, lc3_tns_data_t *data) { - data->nfilters = 1 + (bw >= LC3_BANDWIDTH_SWB); + data->nfilters = 1 + (dt >= LC3_DT_5M && bw >= LC3_BANDWIDTH_SWB); data->lpc_weighting = resolve_lpc_weighting(dt, nbytes); for (int f = 0; f < data->nfilters; f++) { @@ -449,9 +489,13 @@ void lc3_tns_get_data(lc3_bits_t *bits, data->rc_order[f] += lc3_get_symbol(bits, lc3_tns_order_models + data->lpc_weighting); + if (dt <= LC3_DT_5M && data->rc_order[f] > 4) + return -1; for (int i = 0; i < data->rc_order[f]; i++) data->rc[f][i] = (int)lc3_get_symbol(bits, lc3_tns_coeffs_models + i) - 8; } + + return 0; } diff --git a/src/tns.h b/src/tns.h index 534f191..95d4e67 100644 --- a/src/tns.h +++ b/src/tns.h @@ -16,13 +16,6 @@ * ******************************************************************************/ -/** - * LC3 - Temporal Noise Shaping - * - * Reference : Low Complexity Communication Codec (LC3) - * Bluetooth Specification v1.0 - */ - #ifndef __LC3_TNS_H #define __LC3_TNS_H @@ -82,8 +75,9 @@ void lc3_tns_put_data(lc3_bits_t *bits, const lc3_tns_data_t *data); * dt, bw Duration and bandwidth of the frame * nbytes Size in bytes of the frame * data Bitstream data + * return 0: Ok -1: Invalid bitstream data */ -void lc3_tns_get_data(lc3_bits_t *bits, +int lc3_tns_get_data(lc3_bits_t *bits, enum lc3_dt dt, enum lc3_bandwidth bw, int nbytes, lc3_tns_data_t *data); /** diff --git a/tables/mktables.py b/tables/mktables.py index 4f6f855..1cb56ef 100755 --- a/tables/mktables.py +++ b/tables/mktables.py @@ -120,7 +120,7 @@ def mdct_fft_twiddles(): def mdct_rot_twiddles(): - for n in (120, 160, 240, 320, 360, 480, 640, 720, 960): + for n in (40, 80, 120, 160, 240, 320, 360, 480, 640, 720, 960): print('\n--- mdct rot twiddles {:3d} ---'.format(n)) diff --git a/test/attdet.py b/test/attdet.py index 3b6e9d9..78af304 100644 --- a/test/attdet.py +++ b/test/attdet.py @@ -54,7 +54,7 @@ class AttackDetector: def run(self, nbytes, x): - ### 3.3.6.2 Downsampling and filtering input + ### Downsampling and filtering input mf = int(16 * self.ms) @@ -68,7 +68,7 @@ class AttackDetector: self.xn2 = x_att[-2] self.xn1 = x_att[-1] - ### 3.3.6.3 Energy calculation + ### Energy calculation nb = int(self.ms / 2.5) @@ -82,7 +82,7 @@ class AttackDetector: self.en1 = e_att[-1] self.an1 = a_att[-1] - ### 3.3.6.4 Attack Detection + ### Attack Detection p_att = -1 flags = [ (e_att[i] > 8.5 * a_att[i]) for i in range(nb) ] @@ -151,17 +151,19 @@ def check_unit(rng, dt, sr): def check_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_48K + ok = True state = initial_state() - x = np.append(np.zeros(6), C.X_PCM_ATT[dt][0]) - f_att = lc3.attdet_run(dt, sr, C.NBYTES_ATT[dt], state, x) - ok = f_att == C.F_ATT[dt][0] + x = np.append(np.zeros(6), C.X_PCM_ATT[i0][0]) + f_att = lc3.attdet_run(dt, sr, C.NBYTES_ATT[i0], state, x) + ok = ok and f_att == C.F_ATT[i0][0] - x = np.append(x[-6:], C.X_PCM_ATT[dt][1]) - f_att = lc3.attdet_run(dt, sr, C.NBYTES_ATT[dt], state, x) - ok = f_att == C.F_ATT[dt][1] + x = np.append(x[-6:], C.X_PCM_ATT[i0][1]) + f_att = lc3.attdet_run(dt, sr, C.NBYTES_ATT[i0], state, x) + ok = ok and f_att == C.F_ATT[i0][1] return ok @@ -173,11 +175,11 @@ def check(): for dt in range(T.NUM_DT): ok and check_enabling(rng, dt) - for dt in range(T.NUM_DT): + for dt in range(T.DT_7M5, T.NUM_DT): for sr in range(T.SRATE_32K, T.NUM_SRATE): ok = ok and check_unit(rng, dt, sr) - for dt in range(T.NUM_DT): + for dt in range(T.DT_7M5, T.NUM_DT): ok = ok and check_appendix_c(dt) return ok diff --git a/test/bwdet.py b/test/bwdet.py index 88d7c8c..852fde4 100644 --- a/test/bwdet.py +++ b/test/bwdet.py @@ -21,11 +21,15 @@ import tables as T, appendix_c as C BW_START = [ + [ [], [ 24 ], [ 24, 35 ], [ 24, 33, 39 ], [ 22, 31, 37, 41 ] ], + [ [], [ 39 ], [ 35, 47 ], [ 34, 44, 50 ], [ 32, 42, 48, 52 ] ], [ [], [ 51 ], [ 45, 58 ], [ 42, 53, 60 ], [ 40, 51, 57, 61 ] ], [ [], [ 53 ], [ 47, 59 ], [ 44, 54, 60 ], [ 41, 51, 57, 61 ] ] ] BW_STOP = [ + [ [], [ 34 ], [ 32, 39 ], [ 31, 38, 42 ], [ 29, 35, 40, 43 ] ], + [ [], [ 49 ], [ 44, 51 ], [ 42, 49, 53 ], [ 40, 46, 51, 54 ] ], [ [], [ 63 ], [ 55, 63 ], [ 51, 58, 63 ], [ 48, 55, 60, 63 ] ], [ [], [ 63 ], [ 56, 63 ], [ 52, 59, 63 ], [ 49, 55, 60, 63 ] ] ] @@ -33,8 +37,8 @@ BW_STOP = [ TQ = [ 20, 10, 10, 10 ] TC = [ 15, 23, 20, 20 ] -L = [ [ 4, 4, 3, 2 ], [ 4, 4, 3, 1 ] ] - +L = [ [ 4, 4, 3, 1 ], [ 4, 4, 3, 1 ], + [ 4, 4, 3, 2 ], [ 4, 4, 3, 1 ] ] ### ------------------------------------------------------------------------ ### @@ -131,11 +135,13 @@ def check_unit(rng, dt, sr): def check_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K + ok = True - E_B = C.E_B[dt] - P_BW = C.P_BW[dt] + E_B = C.E_B[i0] + P_BW = C.P_BW[i0] bw = lc3.bwdet_run(dt, sr, E_B[0]) ok = ok and bw == P_BW[0] @@ -154,7 +160,7 @@ def check(): for sr in range(T.NUM_SRATE): ok = ok and check_unit(rng, dt, sr) - for dt in range(T.NUM_DT): + for dt in range(T.DT_7M5, T.NUM_DT): ok = ok and check_appendix_c(dt) return ok diff --git a/test/bwdet_py.c b/test/bwdet_py.c index c2decb3..457c160 100644 --- a/test/bwdet_py.c +++ b/test/bwdet_py.c @@ -33,7 +33,7 @@ static PyObject *bwdet_run_py(PyObject *m, PyObject *args) CTYPES_CHECK("dt", (unsigned)dt < LC3_NUM_DT); CTYPES_CHECK("sr", (unsigned)sr < LC3_NUM_SRATE); - CTYPES_CHECK("e", to_1d_ptr(e_obj, NPY_FLOAT, LC3_NUM_BANDS, &e)); + CTYPES_CHECK("e", to_1d_ptr(e_obj, NPY_FLOAT, LC3_MAX_BANDS, &e)); int bw = lc3_bwdet_run(dt, sr, e); diff --git a/test/decoder.py b/test/decoder.py index 378871c..26cdccc 100755 --- a/test/decoder.py +++ b/test/decoder.py @@ -100,14 +100,15 @@ class Decoder: def check_appendix_c(dt): - ok = True + i0 = dt - T.DT_7M5 dec_c = lc3.setup_decoder(int(T.DT_MS[dt] * 1000), 16000) + ok = True - for i in range(len(C.BYTES_AC[dt])): + for i in range(len(C.BYTES_AC[i0])): - pcm = lc3.decode(dec_c, bytes(C.BYTES_AC[dt][i])) - ok = ok and np.max(np.abs(pcm - C.X_HAT_CLIP[dt][i])) < 1 + pcm = lc3.decode(dec_c, bytes(C.BYTES_AC[i0][i])) + ok = ok and np.max(np.abs(pcm - C.X_HAT_CLIP[i0][i])) < 1 return ok @@ -115,7 +116,7 @@ def check(): ok = True - for dt in range(T.NUM_DT): + for dt in range(T.DT_7M5, T.NUM_DT): ok = ok and check_appendix_c(dt) return ok diff --git a/test/encoder.py b/test/encoder.py index 6395524..2d32505 100755 --- a/test/encoder.py +++ b/test/encoder.py @@ -108,14 +108,15 @@ class Encoder: def check_appendix_c(dt): - ok = True + i0 = dt - T.DT_7M5 enc_c = lc3.setup_encoder(int(T.DT_MS[dt] * 1000), 16000) + ok = True - for i in range(len(C.X_PCM[dt])): + for i in range(len(C.X_PCM[i0])): - data = lc3.encode(enc_c, C.X_PCM[dt][i], C.NBYTES[dt]) - ok = ok and data == C.BYTES_AC[dt][i] + data = lc3.encode(enc_c, C.X_PCM[i0][i], C.NBYTES[i0]) + ok = ok and data == C.BYTES_AC[i0][i] return ok @@ -123,7 +124,7 @@ def check(): ok = True - for dt in range(T.NUM_DT): + for dt in range(T.DT_7M5, T.NUM_DT): ok = ok and check_appendix_c(dt) return ok diff --git a/test/energy.py b/test/energy.py index a1bc5c3..5cdbc36 100644 --- a/test/energy.py +++ b/test/energy.py @@ -33,10 +33,10 @@ class EnergyBand: e = [ np.mean(np.square(x[self.I[i]:self.I[i+1]])) for i in range(len(self.I)-1) ] - e_lo = np.sum(e[:len(e) - [4, 2][self.dt]]) - e_hi = np.sum(e[len(e) - [4, 2][self.dt]:]) + e_lo = np.sum(e[:len(e) - [2, 3, 4, 2][self.dt]]) + e_hi = np.sum(e[len(e) - [2, 3, 4, 2][self.dt]:]) - return np.append(e, np.zeros(64-len(e))), (e_hi > 30*e_lo) + return e, (e_hi > 30*e_lo) ### ------------------------------------------------------------------------ ### @@ -63,14 +63,16 @@ def check_unit(rng, dt, sr): def check_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K + ok = True - e = lc3.energy_compute(dt, sr, C.X[dt][0])[0] - ok = ok and np.amax(np.abs(1 - e/C.E_B[dt][0])) < 1e-6 + e = lc3.energy_compute(dt, sr, C.X[i0][0])[0] + ok = ok and np.amax(np.abs(1 - e/C.E_B[i0][0])) < 1e-6 - e = lc3.energy_compute(dt, sr, C.X[dt][1])[0] - ok = ok and np.amax(np.abs(1 - e/C.E_B[dt][1])) < 1e-6 + e = lc3.energy_compute(dt, sr, C.X[i0][1])[0] + ok = ok and np.amax(np.abs(1 - e/C.E_B[i0][1])) < 1e-6 return ok @@ -84,7 +86,7 @@ def check(): for sr in range(T.NUM_SRATE): ok = ok and check_unit(rng, dt, sr) - for dt in range(T.NUM_DT): + for dt in range(T.DT_7M5, T.NUM_DT): ok = ok and check_appendix_c(dt) return ok diff --git a/test/energy_py.c b/test/energy_py.c index 3b65ba5..6f2d97a 100644 --- a/test/energy_py.c +++ b/test/energy_py.c @@ -40,7 +40,7 @@ static PyObject *energy_compute_py(PyObject *m, PyObject *args) int ns = LC3_NS(dt, sr); CTYPES_CHECK("x", to_1d_ptr(x_obj, NPY_FLOAT, ns, &x)); - e_obj = new_1d_ptr(NPY_FLOAT, LC3_NUM_BANDS, &e); + e_obj = new_1d_ptr(NPY_FLOAT, lc3_num_bands[dt][sr], &e); int nn_flag = lc3_energy_compute(dt, sr, x, e); diff --git a/test/ltpf.py b/test/ltpf.py index f60dc71..5c0019a 100644 --- a/test/ltpf.py +++ b/test/ltpf.py @@ -31,7 +31,7 @@ class Resampler_12k8: self.w = 240 // self.p self.n = ((T.DT_MS[dt] * 128) / 10).astype(int) - self.d = [ 44, 24 ][dt] + self.d = [ 24, 44 ][dt == T.DT_7M5] self.x = np.zeros(self.w + T.NS[dt][sr]) self.u = np.zeros(self.n + 2) @@ -59,7 +59,7 @@ class Resampler_12k8: x = self.x u = self.u - ### 3.3.9.3 Resampling + ### Resampling h = np.zeros(240 + p) h[-119:] = T.LTPF_H12K8[:119] @@ -74,7 +74,7 @@ class Resampler_12k8: if self.sr == T.SRATE_8K: u = 0.5 * u - ### 3.3.9.4 High-pass filtering + ### High-pass filtering b = [ 0.9827947082978771, -1.9655894165957540, 0.9827947082978771 ] a = [ 1 , -1.9652933726226904, 0.9658854605688177 ] @@ -110,7 +110,7 @@ class Resampler_6k4: if len(self.y) > n: self.y[-n:] = self.y[:n] - ### 3.3.9.5 Downsampling to 6.4 KHz + ### Downsampling to 6.4 KHz h = [ 0.1236796411180537, 0.2353512128364889, 0.2819382920909148, 0.2353512128364889, 0.1236796411180537 ] @@ -140,11 +140,11 @@ class LtpfAnalysis(Ltpf): super().__init__(dt, sr) - self.resampler_12k8 = Resampler_12k8( - dt, sr, history = 232) + self.resampler_12k8 = Resampler_12k8(dt, sr, + history = 232 + (32 if dt == T.DT_2M5 else 0)) - self.resampler_6k4 = Resampler_6k4( - self.resampler_12k8.n, history = 114) + self.resampler_6k4 = Resampler_6k4(self.resampler_12k8.n, + history = 114 + (16 if dt == T.DT_2M5 else 0)) self.active = False self.tc = 0 @@ -160,30 +160,32 @@ class LtpfAnalysis(Ltpf): return 1 + 10 * int(self.pitch_present) - def correlate(self, x, n, k0, k1): + def correlate(self, x, i0, n, k0, k1): - return [ np.dot(x[:n], np.take(x, np.arange(n) - k)) \ - for k in range(k0, 1+k1) ] + return np.array([ np.dot( + np.take(x, np.arange(i0, n)), + np.take(x, np.arange(i0, n) - k)) for k in range(k0, 1+k1) ]) - def norm_corr(self, x, n, k): + def norm_corr(self, x, i0, n, k): - u = x[:n] - v = np.take(x, np.arange(n) - k) + u = np.take(x, np.arange(i0, n)) + v = np.take(x, np.arange(i0, n) - k) uv = np.dot(u, v) return uv / np.sqrt(np.dot(u, u) * np.dot(v, v)) if uv > 0 else 0 def run(self, x): - ### 3.3.9.3-4 Resampling + ### Resampling x_12k8 = self.resampler_12k8.resample(x) - ### 3.3.9.5-6 Pitch detection algorithm + ### Pitch detection algorithm - x = self.resampler_6k4.resample(x_12k8) - n = self.resampler_6k4.n + x = self.resampler_6k4.resample(x_12k8) + i0 = [-16, 0][self.dt > T.DT_2M5] + n = self.resampler_6k4.n - r = self.correlate(x, n, 17, 114) + r = self.correlate(x, i0, n, 17, 114) rw = r * (1 - 0.5 * np.arange(len(r)) / (len(r) - 1)) tc = self.tc @@ -191,23 +193,24 @@ class LtpfAnalysis(Ltpf): k1 = min(len(r)-1, tc+4) t = [ 17 + np.argmax(rw), 17 + k0 + np.argmax(r[k0:1+k1]) ] - nc = [ self.norm_corr(x, n, t[i]) for i in range(2) ] + nc = [ self.norm_corr(x, i0, n, t[i]) for i in range(2) ] ti = int(nc[1] > 0.85 * nc[0]) self.tc = t[ti] - 17 self.pitch_present = bool(nc[ti] > 0.6) - ### 3.3.9.7 Pitch-lag parameter + ### Pitch-lag parameter if self.pitch_present: tc = self.tc + 17 - x = x_12k8 - n = self.resampler_12k8.n + x = x_12k8 + i0 = [-32, 0][self.dt > T.DT_2M5] + n = self.resampler_12k8.n k0 = max( 32, 2*tc-4) k1 = min(228, 2*tc+4) - r = self.correlate(x, n, k0-4, k1+4) + r = self.correlate(x, i0, n, k0-4, k1+4) e = k0 + np.argmax(r[4:-4]) h = np.zeros(42) @@ -232,17 +235,21 @@ class LtpfAnalysis(Ltpf): e = f = 0 self.pitch_index = 0 - ### 3.3.9.8 Activation bit + ### Activation bit h = np.zeros(24) h[-7:] = T.LTPF_HI[:7] h[ :8] = T.LTPF_HI[7:] + x = x_12k8 + i0 = [-32, 0][self.dt > T.DT_2M5] + n = self.resampler_12k8.n + k = np.arange(-2, 3) u = [ np.dot( np.take(x, i-k), np.take(h, 4*k) ) \ - for i in range(n) ] + for i in range(i0, n) ] v = [ np.dot( np.take(x, i-k), np.take(h, 4*k-f) ) \ - for i in range(-e, n-e) ] + for i in range(i0-e, n-e) ] nc = max(0, np.dot(u, v)) / np.sqrt(np.dot(u, u) * np.dot(v, v)) \ if self.pitch_present else 0 @@ -327,7 +334,7 @@ class LtpfSynthesis(Ltpf): sr = self.sr dt = self.dt - ### 3.4.9.4 Filter parameters + ### Filter parameters pitch_index = self.pitch_index @@ -346,7 +353,12 @@ class LtpfSynthesis(Ltpf): self.p_e[0] = int(p * 4 + 0.5) // 4 self.p_f[0] = int(p * 4 + 0.5) - 4*self.p_e[0] - nbits = round(nbytes*80 / T.DT_MS[dt]) + nbits = round(nbytes*8 * 10 / T.DT_MS[dt]) + if dt == T.DT_2M5: + nbits = int(nbits * (1 - 0.4)) + elif dt == T.DT_5M: + nbits = nbits - 160 + g_idx = max(nbits // 80, 3+sr) - (3+sr) g = [ 0.4, 0.35, 0.3, 0.25 ][g_idx] if g_idx < 4 else 0 @@ -355,7 +367,7 @@ class LtpfSynthesis(Ltpf): self.c_n[0] = 0.85 * g * LtpfSynthesis.C_N[sr][g_idx] self.c_d[0] = g * LtpfSynthesis.C_D[sr][self.p_f[0]] - ### 3.4.9.2 Transition handling + ### Transition handling n0 = (T.SRATE_KHZ[sr] * 1000) // 400 ns = T.NS[dt][sr] @@ -402,8 +414,7 @@ class LtpfSynthesis(Ltpf): np.dot(c_d[0], np.take(y , k - d[0] - np.arange(l_d))) y[k] = yc[k] - (k/n0) * u - - ### 3.4.9.3 Remainder of the frame + ### Remainder of the frame for k in range(n0, ns): @@ -466,28 +477,30 @@ def check_resampler(rng, dt, sr): def check_resampler_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K + ok = True nt = (5 * T.SRATE_KHZ[sr]) // 4 - n = [ 96, 128 ][dt] - k = [ 44, 24 ][dt] + n + n = [ 96, 128 ][i0] + k = [ 44, 24 ][i0] + n state = initial_hp50_state() - x = np.append(np.zeros(nt), C.X_PCM[dt][0]) + x = np.append(np.zeros(nt), C.X_PCM[i0][0]) y = np.zeros(384) y = lc3.ltpf_resample(dt, sr, state, x, y) - u = y[-k:len(C.X_TILDE_12K8D[dt][0])-k] + u = y[-k:len(C.X_TILDE_12K8D[i0][0])-k] - ok = ok and np.amax(np.abs(u - C.X_TILDE_12K8D[dt][0]/2)) < 2 + ok = ok and np.amax(np.abs(u - C.X_TILDE_12K8D[i0][0]/2)) < 2 - x = np.append(x[-nt:], C.X_PCM[dt][1]) + x = np.append(x[-nt:], C.X_PCM[i0][1]) y[:-n] = y[n:] y = lc3.ltpf_resample(dt, sr, state, x, y) - u = y[-k:len(C.X_TILDE_12K8D[dt][1])-k] + u = y[-k:len(C.X_TILDE_12K8D[i0][1])-k] - ok = ok and np.amax(np.abs(u - C.X_TILDE_12K8D[dt][1]/2)) < 2 + ok = ok and np.amax(np.abs(u - C.X_TILDE_12K8D[i0][1]/2)) < 2 return ok @@ -503,7 +516,7 @@ def check_analysis(rng, dt, sr): ltpf = LtpfAnalysis(dt, sr) t = np.arange(100 * ns) / (T.SRATE_KHZ[sr] * 1000) - s = signal.chirp(t, f0=10, f1=3e3, t1=t[-1], method='logarithmic') + s = signal.chirp(t, f0=10, f1=2500, t1=t[-1], method='logarithmic') for i in range(20): @@ -516,7 +529,7 @@ def check_analysis(rng, dt, sr): (pitch_present_c, data_c) = lc3.ltpf_analyse(dt, sr, state_c, x_c) ok = ok and (not pitch_present or state_c['tc'] == ltpf.tc) - ok = ok and np.amax(np.abs(state_c['nc'][0] - ltpf.nc[0])) < 1e-2 + ok = ok and np.amax(np.abs(state_c['nc'][0] - ltpf.nc[0])) < 1e-1 ok = ok and pitch_present_c == pitch_present ok = ok and data_c['active'] == data['active'] ok = ok and data_c['pitch_index'] == data['pitch_index'] @@ -537,6 +550,7 @@ def check_synthesis(rng, dt, sr): x_c = np.zeros(nd+ns) for i in range(50): + pitch_present = bool(rng.integers(0, 10) >= 1) if not pitch_present: synthesis.disable() @@ -563,37 +577,40 @@ def check_synthesis(rng, dt, sr): def check_analysis_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K - nt = (5 * T.SRATE_KHZ[sr]) // 4 + ok = True + nt = (5 * T.SRATE_KHZ[sr]) // 4 + state = initial_state() - x = np.append(np.zeros(nt), C.X_PCM[dt][0]) + x = np.append(np.zeros(nt), C.X_PCM[i0][0]) (pitch_present, data) = lc3.ltpf_analyse(dt, sr, state, x) - ok = ok and C.T_CURR[dt][0] - state['tc'] == 17 - ok = ok and np.amax(np.abs(state['nc'][0] - C.NC_LTPF[dt][0])) < 1e-5 - ok = ok and pitch_present == C.PITCH_PRESENT[dt][0] - ok = ok and data['pitch_index'] == C.PITCH_INDEX[dt][0] - ok = ok and data['active'] == C.LTPF_ACTIVE[dt][0] + ok = ok and C.T_CURR[i0][0] - state['tc'] == 17 + ok = ok and np.amax(np.abs(state['nc'][0] - C.NC_LTPF[i0][0])) < 1e-5 + ok = ok and pitch_present == C.PITCH_PRESENT[i0][0] + ok = ok and data['pitch_index'] == C.PITCH_INDEX[i0][0] + ok = ok and data['active'] == C.LTPF_ACTIVE[i0][0] - x = np.append(x[-nt:], C.X_PCM[dt][1]) + x = np.append(x[-nt:], C.X_PCM[i0][1]) (pitch_present, data) = lc3.ltpf_analyse(dt, sr, state, x) - ok = ok and C.T_CURR[dt][1] - state['tc'] == 17 - ok = ok and np.amax(np.abs(state['nc'][0] - C.NC_LTPF[dt][1])) < 1e-5 - ok = ok and pitch_present == C.PITCH_PRESENT[dt][1] - ok = ok and data['pitch_index'] == C.PITCH_INDEX[dt][1] - ok = ok and data['active'] == C.LTPF_ACTIVE[dt][1] + ok = ok and C.T_CURR[i0][1] - state['tc'] == 17 + ok = ok and np.amax(np.abs(state['nc'][0] - C.NC_LTPF[i0][1])) < 1e-5 + ok = ok and pitch_present == C.PITCH_PRESENT[i0][1] + ok = ok and data['pitch_index'] == C.PITCH_INDEX[i0][1] + ok = ok and data['active'] == C.LTPF_ACTIVE[i0][1] return ok def check_synthesis_appendix_c(dt): sr = T.SRATE_16K - ok = True + ok = True if dt != T.DT_10M: return ok @@ -650,7 +667,7 @@ def check(): ok = ok and check_analysis(rng, dt, sr) ok = ok and check_synthesis(rng, dt, sr) - for dt in range(T.NUM_DT): + for dt in range(T.DT_7M5, T.NUM_DT): ok = ok and check_resampler_appendix_c(dt) ok = ok and check_analysis_appendix_c(dt) ok = ok and check_synthesis_appendix_c(dt) diff --git a/test/ltpf_py.c b/test/ltpf_py.c index c51eadd..b25c16a 100644 --- a/test/ltpf_py.c +++ b/test/ltpf_py.c @@ -38,7 +38,7 @@ static PyObject *resample_py(PyObject *m, PyObject *args) int ns = LC3_NS(dt, sr), nt = LC3_NT(dt); int ny = sizeof((struct lc3_ltpf_analysis){ }.x_12k8) / sizeof(int16_t); - int n = dt == LC3_DT_7M5 ? 96 : 128; + int n = (1 + dt) * 32; CTYPES_CHECK("x", x_obj = to_1d_ptr(x_obj, NPY_INT16, ns+nt, &x)); CTYPES_CHECK("y", y_obj = to_1d_ptr(y_obj, NPY_INT16, ny, &y)); diff --git a/test/mdct.py b/test/mdct.py index 4e6d191..8a19383 100644 --- a/test/mdct.py +++ b/test/mdct.py @@ -24,8 +24,10 @@ import tables as T, appendix_c as C class Mdct: - W = [ [ T.W_7M5_60, T.W_7M5_120, T.W_7M5_180, T.W_7M5_240, T.W_7M5_360 ], - [ T.W_10M_80, T.W_10M_160, T.W_10M_240, T.W_10M_320, T.W_10M_480 ] ] + W = [ [ T.W_2M5_8K, T.W_2M5_16K, T.W_2M5_24K, T.W_2M5_32K, T.W_2M5_48K ], + [ T.W_5M_8K , T.W_5M_16K , T.W_5M_24K , T.W_5M_32K , T.W_5M_48K ], + [ T.W_7M5_8K, T.W_7M5_16K, T.W_7M5_24K, T.W_7M5_32K, T.W_7M5_48K ], + [ T.W_10M_8K, T.W_10M_16K, T.W_10M_24K, T.W_10M_32K, T.W_10M_48K ] ] def __init__(self, dt, sr): @@ -117,16 +119,19 @@ def check_forward_unit(rng, dt, sr): def check_forward_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K - ns = T.NS[dt][sr] - nd = T.ND[dt][sr] + ok = True - (y, d) = lc3.mdct_forward(dt, sr, C.X_PCM[dt][0], np.zeros(nd)) - ok = ok and np.amax(np.abs(y - C.X[dt][0])) < 1e-1 + ns = T.NS[dt][sr] + nd = T.ND[dt][sr] - (y, d) = lc3.mdct_forward(dt, sr, C.X_PCM[dt][1], d) - ok = ok and np.amax(np.abs(y - C.X[dt][1])) < 1e-1 + (y, d) = lc3.mdct_forward(dt, sr, C.X_PCM[i0][0], np.zeros(nd)) + ok = ok and np.amax(np.abs(y - C.X[i0][0])) < 1e-1 + + (y, d) = lc3.mdct_forward(dt, sr, C.X_PCM[i0][1], d) + ok = ok and np.amax(np.abs(y - C.X[i0][1])) < 1e-1 return ok @@ -134,7 +139,7 @@ def check_forward_appendix_c(dt): def check_inverse_unit(rng, dt, sr): ns = T.NS[dt][sr] - nd = [ (23 * ns) // 30, (5 * ns) // 8 ][dt] + nd = T.ND[dt][sr] ok = True x = (2 * rng.random(ns)) - 1 @@ -157,22 +162,25 @@ def check_inverse_unit(rng, dt, sr): def check_inverse_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K - ns = T.NS[dt][sr] - nd = [ (23 * ns) // 30, (5 * ns) // 8 ][dt] + ok = True - (y, d0) = lc3.mdct_inverse(dt, sr, C.X_HAT_SNS[dt][0], np.zeros(nd)) - yr = C.T_HAT_MDCT[dt][0][ns-nd:2*ns-nd] - dr = C.T_HAT_MDCT[dt][0][2*ns-nd:] + ns = T.NS[dt][sr] + nd = T.ND[dt][sr] + + (y, d0) = lc3.mdct_inverse(dt, sr, C.X_HAT_SNS[i0][0], np.zeros(nd)) + yr = C.T_HAT_MDCT[i0][0][ns-nd:2*ns-nd] + dr = C.T_HAT_MDCT[i0][0][2*ns-nd:] ok = ok and np.amax(np.abs(yr - y )) < 1e-1 ok = ok and np.amax(np.abs(dr - d0)) < 1e-1 - (y, d1) = lc3.mdct_inverse(dt, sr, C.X_HAT_SNS[dt][1], d0) - yr[ :nd] = C.T_HAT_MDCT[dt][1][ns-nd:ns] + d0 - yr[nd:ns] = C.T_HAT_MDCT[dt][1][ns:2*ns-nd] - dr = C.T_HAT_MDCT[dt][1][2*ns-nd:] + (y, d1) = lc3.mdct_inverse(dt, sr, C.X_HAT_SNS[i0][1], d0) + yr[ :nd] = C.T_HAT_MDCT[i0][1][ns-nd:ns] + d0 + yr[nd:ns] = C.T_HAT_MDCT[i0][1][ns:2*ns-nd] + dr = C.T_HAT_MDCT[i0][1][2*ns-nd:] ok = ok and np.amax(np.abs(yr - y )) < 1e-1 ok = ok and np.amax(np.abs(dr - d1)) < 1e-1 @@ -191,7 +199,7 @@ def check(): ok = ok and check_forward_unit(rng, dt, sr) ok = ok and check_inverse_unit(rng, dt, sr) - for dt in range(T.NUM_DT): + for dt in range(T.DT_7M5, T.NUM_DT): ok = ok and check_forward_appendix_c(dt) ok = ok and check_inverse_appendix_c(dt) diff --git a/test/sns.py b/test/sns.py index 8740aab..ed66413 100644 --- a/test/sns.py +++ b/test/sns.py @@ -52,7 +52,7 @@ class Sns: def spectral_shaping(self, scf, inv, x): - ## 3.3.7.4 Scale factors interpolation + ## Scale factors interpolation scf_i = np.empty(4*len(scf)) scf_i[0 ] = scf[0] @@ -64,20 +64,35 @@ class Sns: scf_i[62 ] = scf[15 ] + 1/8 * (scf[15] - scf[14 ]) scf_i[63 ] = scf[15 ] + 3/8 * (scf[15] - scf[14 ]) - n2 = 64 - min(len(x), 64) + nb = len(T.I[self.dt][self.sr]) - 1 - for i in range(n2): - scf_i[i] = 0.5 * (scf_i[2*i] + scf_i[2*i+1]) - scf_i = np.append(scf_i[:n2], scf_i[2*n2:]) + if nb < 32: + n4 = round(abs(1-32/nb)*nb) + n2 = nb - n4 + + for i in range(n4): + scf_i[i] = np.mean(scf_i[4*i:4*i+4]) + + for i in range(n4, n4+n2): + scf_i[i] = np.mean(scf_i[2*n4+2*i:2*n4+2*i+2]) + + scf_i = scf_i[:n4+n2] + + elif nb < 64: + n2 = 64 - nb + + for i in range(n2): + scf_i[i] = np.mean(scf_i[2*i:2*i+2]) + scf_i = np.append(scf_i[:n2], scf_i[2*n2:]) g_sns = np.power(2, [ -scf_i, scf_i ][inv]) - ## 3.3.7.4 Spectral shaping + ## Spectral shaping y = np.empty(len(x)) I = T.I[self.dt][self.sr] - for b in range(len(g_sns)): + for b in range(nb): y[I[b]:I[b+1]] = x[I[b]:I[b+1]] * g_sns[b] return y @@ -93,36 +108,49 @@ class SnsAnalysis(Sns): dt = self.dt - ## 3.3.7.2.1 Padding + ## Padding - n2 = 64 - len(e) + if len(e) < 32: + n4 = round(abs(1-32/len(e))*len(e)) + n2 = len(e) - n4 - e = np.append(np.empty(n2), e) - for i in range(n2): - e[2*i+0] = e[2*i+1] = e[n2+i] + e = np.append(np.zeros(3*n4+n2), e) + for i in range(n4): + e[4*i+0] = e[4*i+1] = \ + e[4*i+2] = e[4*i+3] = e[3*n4+n2+i] - ## 3.3.7.2.2 Smoothing + for i in range(2*n4, 2*n4+n2): + e[2*i+0] = e[2*i+1] = e[2*n4+n2+i] + + elif len(e) < 64: + n2 = 64 - len(e) + + e = np.append(np.empty(n2), e) + for i in range(n2): + e[2*i+0] = e[2*i+1] = e[n2+i] + + ## Smoothing e_s = np.zeros(len(e)) e_s[0 ] = 0.75 * e[0 ] + 0.25 * e[1 ] e_s[1:63] = 0.25 * e[0:62] + 0.5 * e[1:63] + 0.25 * e[2:64] e_s[ 63] = 0.25 * e[ 62] + 0.75 * e[ 63] - ## 3.3.7.2.3 Pre-emphasis + ## Pre-emphasis g_tilt = [ 14, 18, 22, 26, 30 ][self.sr] e_p = e_s * (10 ** ((np.arange(64) * g_tilt) / 630)) - ## 3.3.7.2.4 Noise floor + ## Noise floor noise_floor = max(np.average(e_p) * (10 ** (-40/10)), 2 ** -32) e_p = np.fmax(e_p, noise_floor * np.ones(len(e))) - ## 3.3.7.2.5 Logarithm + ## Logarithm e_l = np.log2(10 ** -31 + e_p) / 2 - ## 3.3.7.2.6 Band energy grouping + ## Band energy grouping w = [ 1/12, 2/12, 3/12, 3/12, 2/12, 1/12 ] @@ -131,18 +159,18 @@ class SnsAnalysis(Sns): e_4[1:15] = [ np.sum(w * e_l[4*i-1:4*i+5]) for i in range(1, 15) ] e_4[ 15] = np.sum(w[:5] * e_l[59:64]) + w[5] * e_l[63] - ## 3.3.7.2.7 Mean removal and scaling, attack handling + ## Mean removal and scaling, attack handling scf = 0.85 * (e_4 - np.average(e_4)) scf_a = np.zeros(len(scf)) - scf_a[0 ] = np.average(scf[:3]) - scf_a[1 ] = np.average(scf[:4]) - scf_a[2:14] = [ np.average(scf[i:i+5]) for i in range(12) ] - scf_a[ 14] = np.average(scf[12:]) - scf_a[ 15] = np.average(scf[13:]) + scf_a[0 ] = np.mean(scf[:3]) + scf_a[1 ] = np.mean(scf[:4]) + scf_a[2:14] = [ np.mean(scf[i:i+5]) for i in range(12) ] + scf_a[ 14] = np.mean(scf[12:]) + scf_a[ 15] = np.mean(scf[13:]) - scf_a = (0.5 if self.dt == T.DT_10M else 0.3) * \ + scf_a = (0.5 if self.dt != T.DT_7M5 else 0.3) * \ (scf_a - np.average(scf_a)) return scf_a if att else scf @@ -167,7 +195,7 @@ class SnsAnalysis(Sns): def quantize(self, scf): - ## 3.3.7.3.2 Stage 1 + ## Stage 1 dmse_lf = [ np.sum((scf[:8] - T.SNS_LFCB[i]) ** 2) for i in range(32) ] dmse_hf = [ np.sum((scf[8:] - T.SNS_HFCB[i]) ** 2) for i in range(32) ] @@ -178,19 +206,19 @@ class SnsAnalysis(Sns): st1 = np.append(T.SNS_LFCB[self.ind_lf], T.SNS_HFCB[self.ind_hf]) r1 = scf - st1 - ## 3.3.7.3.3 Stage 2 + ## Stage 2 t2_rot = fftpack.dct(r1, norm = 'ortho') x = np.abs(t2_rot) - ## 3.3.7.3.3 Stage 2 Shape search, step 1 + ## Stage 2 Shape search, step 1 K = 6 proj_fac = (K - 1) / sum(np.abs(t2_rot)) y3 = np.floor(x * proj_fac).astype(int) - ## 3.3.7.3.3 Stage 2 Shape search, step 2 + ## Stage 2 Shape search, step 2 corr_xy = np.sum(y3 * x) energy_y = np.sum(y3 * y3) @@ -204,7 +232,7 @@ class SnsAnalysis(Sns): energy_y += 2*y3[n_best] + 1 y3[n_best] += 1 - ## 3.3.7.3.3 Stage 2 Shape search, step 3 + ## Stage 2 Shape search, step 3 K = 8 @@ -219,16 +247,16 @@ class SnsAnalysis(Sns): y2[n_best] += 1 - ## 3.3.7.3.3 Stage 2 Shape search, step 4 + ## Stage 2 Shape search, step 4 y1 = np.append(y2[:10], [0] * 6) - ## 3.3.7.3.3 Stage 2 Shape search, step 5 + ## Stage 2 Shape search, step 5 corr_xy -= sum(y2[10:] * x[10:]) energy_y -= sum(y2[10:] * y2[10:]) - ## 3.3.7.3.3 Stage 2 Shape search, step 6 + ## Stage 2 Shape search, step 6 K = 10 @@ -240,7 +268,7 @@ class SnsAnalysis(Sns): energy_y += 2*y1[n_best] + 1 y1[n_best] += 1 - ## 3.3.7.3.3 Stage 2 Shape search, step 7 + ## Stage 2 Shape search, step 7 y0 = np.append(y1[:10], [ 0 ] * 6) @@ -249,18 +277,18 @@ class SnsAnalysis(Sns): y0[n_best] += 1 - ## 3.3.7.3.3 Stage 2 Shape search, step 8 + ## Stage 2 Shape search, step 8 y0 *= np.sign(t2_rot).astype(int) y1 *= np.sign(t2_rot).astype(int) y2 *= np.sign(t2_rot).astype(int) y3 *= np.sign(t2_rot).astype(int) - ## 3.3.7.3.3 Stage 2 Shape search, step 9 + ## Stage 2 Shape search, step 9 xq = [ y / np.sqrt(sum(y ** 2)) for y in (y0, y1, y2, y3) ] - ## 3.3.7.3.3 Shape and gain combination determination + ## Shape and gain combination determination G = [ T.SNS_VQ_REG_ADJ_GAINS, T.SNS_VQ_REG_LF_ADJ_GAINS, T.SNS_VQ_NEAR_ADJ_GAINS, T.SNS_VQ_FAR_ADJ_GAINS ] @@ -273,7 +301,7 @@ class SnsAnalysis(Sns): gain = G[self.shape][self.gain] - ## 3.3.7.3.3 Enumeration of the selected PVQ pulse configurations + ## Enumeration of the selected PVQ pulse configurations if self.shape == 0: (self.idx_a, self.ls_a) = self.enum_mpvq(y0[:10]) @@ -288,7 +316,7 @@ class SnsAnalysis(Sns): (self.idx_a, self.ls_a) = self.enum_mpvq(y3) (self.idx_b, self.ls_b) = (None, None) - ## 3.3.7.3.4 Synthesis of the Quantized scale factor + ## Synthesis of the Quantized scale factor scf_q = st1 + gain * fftpack.idct(xq[self.shape], norm = 'ortho') @@ -372,7 +400,7 @@ class SnsSynthesis(Sns): def unquantize(self): - ## 3.7.4.2.1-2 SNS VQ Decoding + ## SNS VQ Decoding y = np.empty(16, dtype=np.intc) @@ -387,11 +415,11 @@ class SnsSynthesis(Sns): elif self.shape == 3: y = self.deenum_mpvq(self.idx_a, self.ls_a, 6, 16) - ## 3.7.4.2.3 Unit energy normalization + ## Unit energy normalization y = y / np.sqrt(sum(y ** 2)) - ## 3.7.4.2.4 Reconstruction of the quantized scale factors + ## Reconstruction of the quantized scale factors G = [ T.SNS_VQ_REG_ADJ_GAINS, T.SNS_VQ_REG_LF_ADJ_GAINS, T.SNS_VQ_NEAR_ADJ_GAINS, T.SNS_VQ_FAR_ADJ_GAINS ] @@ -465,7 +493,7 @@ def check_analysis(rng, dt, sr): for i in range(10): x = rng.random(T.NE[dt][sr]) * 1e4 - e = rng.random(min(len(x), 64)) * 1e10 + e = rng.random(len(T.I[dt][sr]) - 1) * 1e10 for att in (0, 1): y = analysis.run(e, att, x) @@ -506,72 +534,78 @@ def check_synthesis(rng, dt, sr): y = synthesis.run(x) y_c = lc3.sns_synthesize(dt, sr, synthesis.get_data(), x) - ok = ok and np.amax(np.abs(y - y_c)) < 2e0 + ok = ok and np.amax(np.abs(1 - y/y_c)) < 1e-5 return ok def check_analysis_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K + ok = True - for i in range(len(C.E_B[dt])): + for i in range(len(C.E_B[i0])): - scf = lc3.sns_compute_scale_factors(dt, sr, C.E_B[dt][i], False) - ok = ok and np.amax(np.abs(scf - C.SCF[dt][i])) < 1e-4 + scf = lc3.sns_compute_scale_factors(dt, sr, C.E_B[i0][i], False) + ok = ok and np.amax(np.abs(scf - C.SCF[i0][i])) < 1e-4 (lf, hf) = lc3.sns_resolve_codebooks(scf) - ok = ok and lf == C.IND_LF[dt][i] and hf == C.IND_HF[dt][i] + ok = ok and lf == C.IND_LF[i0][i] and hf == C.IND_HF[i0][i] (y, yn, shape, gain) = lc3.sns_quantize(scf, lf, hf) - ok = ok and np.any(y[0][:16] - C.SNS_Y0[dt][i] == 0) - ok = ok and np.any(y[1][:10] - C.SNS_Y1[dt][i] == 0) - ok = ok and np.any(y[2][:16] - C.SNS_Y2[dt][i] == 0) - ok = ok and np.any(y[3][:16] - C.SNS_Y3[dt][i] == 0) - ok = ok and shape == 2*C.SUBMODE_MSB[dt][i] + C.SUBMODE_LSB[dt][i] - ok = ok and gain == C.G_IND[dt][i] + ok = ok and np.any(y[0][:16] - C.SNS_Y0[i0][i] == 0) + ok = ok and np.any(y[1][:10] - C.SNS_Y1[i0][i] == 0) + ok = ok and np.any(y[2][:16] - C.SNS_Y2[i0][i] == 0) + ok = ok and np.any(y[3][:16] - C.SNS_Y3[i0][i] == 0) + ok = ok and shape == 2*C.SUBMODE_MSB[i0][i] + C.SUBMODE_LSB[i0][i] + ok = ok and gain == C.G_IND[i0][i] scf_q = lc3.sns_unquantize(lf, hf, yn[shape], shape, gain) - ok = ok and np.amax(np.abs(scf_q - C.SCF_Q[dt][i])) < 1e-5 + ok = ok and np.amax(np.abs(scf_q - C.SCF_Q[i0][i])) < 1e-5 - x = lc3.sns_spectral_shaping(dt, sr, C.SCF_Q[dt][i], False, C.X[dt][i]) - ok = ok and np.amax(np.abs(1 - x/C.X_S[dt][i])) < 1e-5 + x = lc3.sns_spectral_shaping( + dt, sr, C.SCF_Q[i0][i], False, C.X[i0][i]) + ok = ok and np.amax(np.abs(1 - x/C.X_S[i0][i])) < 1e-5 - (x, data) = lc3.sns_analyze(dt, sr, C.E_B[dt][i], False, C.X[dt][i]) - ok = ok and data['lfcb'] == C.IND_LF[dt][i] - ok = ok and data['hfcb'] == C.IND_HF[dt][i] - ok = ok and data['shape'] == \ - 2*C.SUBMODE_MSB[dt][i] + C.SUBMODE_LSB[dt][i] - ok = ok and data['gain'] == C.G_IND[dt][i] - ok = ok and data['idx_a'] == C.IDX_A[dt][i] - ok = ok and data['ls_a'] == C.LS_IND_A[dt][i] - ok = ok and (C.IDX_B[dt][i] is None or - data['idx_b'] == C.IDX_B[dt][i]) - ok = ok and (C.LS_IND_B[dt][i] is None or - data['ls_b'] == C.LS_IND_B[dt][i]) - ok = ok and np.amax(np.abs(1 - x/C.X_S[dt][i])) < 1e-5 + (x, data) = lc3.sns_analyze( + dt, sr, C.E_B[i0][i], False, C.X[i0][i]) + ok = ok and data['lfcb'] == C.IND_LF[i0][i] + ok = ok and data['hfcb'] == C.IND_HF[i0][i] + ok = ok and data['shape'] == 2*C.SUBMODE_MSB[i0][i] + \ + C.SUBMODE_LSB[i0][i] + ok = ok and data['gain'] == C.G_IND[i0][i] + ok = ok and data['idx_a'] == C.IDX_A[i0][i] + ok = ok and data['ls_a'] == C.LS_IND_A[i0][i] + ok = ok and (C.IDX_B[i0][i] is None or + data['idx_b'] == C.IDX_B[i0][i]) + ok = ok and (C.LS_IND_B[i0][i] is None or + data['ls_b'] == C.LS_IND_B[i0][i]) + ok = ok and np.amax(np.abs(1 - x/C.X_S[i0][i])) < 1e-5 return ok def check_synthesis_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K + ok = True - for i in range(len(C.X_HAT_TNS[dt])): + for i in range(len(C.X_HAT_TNS[i0])): data = { - 'lfcb' : C.IND_LF[dt][i], 'hfcb' : C.IND_HF[dt][i], - 'shape' : 2*C.SUBMODE_MSB[dt][i] + C.SUBMODE_LSB[dt][i], - 'gain' : C.G_IND[dt][i], - 'idx_a' : C.IDX_A[dt][i], - 'ls_a' : C.LS_IND_A[dt][i], - 'idx_b' : C.IDX_B[dt][i] if C.IDX_B[dt][i] is not None else 0, - 'ls_b' : C.LS_IND_B[dt][i] if C.LS_IND_B[dt][i] is not None else 0, + 'lfcb' : C.IND_LF[i0][i], 'hfcb' : C.IND_HF[i0][i], + 'shape' : 2*C.SUBMODE_MSB[i0][i] + C.SUBMODE_LSB[i0][i], + 'gain' : C.G_IND[i0][i], + 'idx_a' : C.IDX_A[i0][i], + 'ls_a' : C.LS_IND_A[i0][i], + 'idx_b' : C.IDX_B[i0][i] if C.IDX_B[i0][i] is not None else 0, + 'ls_b' : C.LS_IND_B[i0][i] if C.LS_IND_B[i0][i] is not None else 0, } - x = lc3.sns_synthesize(dt, sr, data, C.X_HAT_TNS[dt][i]) - ok = ok and np.amax(np.abs(x - C.X_HAT_SNS[dt][i])) < 1e0 + x = lc3.sns_synthesize(dt, sr, data, C.X_HAT_TNS[i0][i]) + ok = ok and np.amax(np.abs(x - C.X_HAT_SNS[i0][i])) < 1e0 return ok @@ -585,9 +619,9 @@ def check(): ok = ok and check_analysis(rng, dt, sr) ok = ok and check_synthesis(rng, dt, sr) - for dt in range(T.NUM_DT): - ok = ok and check_analysis_appendix_c(dt) - ok = ok and check_synthesis_appendix_c(dt) + for dt in range(T.DT_7M5, T.NUM_DT): + check_analysis_appendix_c(dt) + check_synthesis_appendix_c(dt) return ok diff --git a/test/sns_py.c b/test/sns_py.c index 2ef3e83..d70838b 100644 --- a/test/sns_py.c +++ b/test/sns_py.c @@ -36,7 +36,7 @@ static PyObject *compute_scale_factors_py(PyObject *m, PyObject *args) CTYPES_CHECK("dt", (unsigned)dt < LC3_NUM_DT); CTYPES_CHECK("sr", (unsigned)sr < LC3_NUM_SRATE); - int nb = LC3_MIN(lc3_band_lim[dt][sr][LC3_NUM_BANDS], LC3_NUM_BANDS); + int nb = lc3_num_bands[dt][sr]; CTYPES_CHECK("eb", to_1d_ptr(eb_obj, NPY_FLOAT, nb, &eb)); scf_obj = new_1d_ptr(NPY_FLOAT, 16, &scf); @@ -150,7 +150,7 @@ static PyObject *analyze_py(PyObject *m, PyObject *args) CTYPES_CHECK("sr", (unsigned)sr < LC3_NUM_SRATE); int ne = LC3_NE(dt, sr); - int nb = LC3_MIN(ne, LC3_NUM_BANDS); + int nb = lc3_num_bands[dt][sr]; CTYPES_CHECK("eb", to_1d_ptr(eb_obj, NPY_FLOAT, nb, &eb)); CTYPES_CHECK("x", x_obj = to_1d_ptr(x_obj, NPY_FLOAT, ne, &x)); diff --git a/test/spec.py b/test/spec.py index 22c5e3e..1adb01b 100644 --- a/test/spec.py +++ b/test/spec.py @@ -42,18 +42,17 @@ class SpectrumQuantization: def get_noise_indices(self, bw, xq, lastnz): - nf_start = [ 18, 24 ][self.dt] - nf_width = [ 2, 3 ][self.dt] + nf_start = [ 6, 12, 18, 24 ][self.dt] + nf_width = [ 1, 1, 2, 3 ][self.dt] bw_stop = int([ 80, 160, 240, 320, 400 ][bw] * (T.DT_MS[self.dt] / 10)) xq = np.append(xq[:lastnz], np.zeros(len(xq) - lastnz)) + xq[:nf_start-nf_width] = 1 + xq[min(bw_stop+nf_width+1,bw_stop):] = 1 - i_nf = [ np.all(xq[k-nf_width:min(bw_stop, k+nf_width+1)] == 0) - for k in range(nf_start, bw_stop) ] - - return (i_nf, nf_start, bw_stop) - + return [ np.all(xq[max(k-nf_width, 0):min(k+nf_width+1, bw_stop)] == 0) + for k in range(len(xq)) ] class SpectrumAnalysis(SpectrumQuantization): @@ -118,7 +117,7 @@ class SpectrumAnalysis(SpectrumQuantization): if reset_off: g_idx = g_min - return (g_idx + g_off, reset_off) + return (g_min, g_idx + g_off, reset_off) def quantize(self, g_int, x): @@ -222,7 +221,7 @@ class SpectrumAnalysis(SpectrumQuantization): (g_idx > 0 and nbits < nbits_spec - (delta + 2)): if nbits < nbits_spec - (delta + 2): - return - 1 + return -1 if g_idx == 254 or nbits < nbits_spec + delta: return 1 @@ -234,12 +233,10 @@ class SpectrumAnalysis(SpectrumQuantization): def estimate_noise(self, bw, xq, lastnz, x): - (i_nf, nf_start, nf_stop) = self.get_noise_indices(bw, xq, lastnz) + i_nf = self.get_noise_indices(bw, xq, lastnz) + l_nf = sum(abs(x * i_nf)) / sum(i_nf) if sum(i_nf) > 0 else 0 - nf = 8 - 16 * sum(abs(x[nf_start:nf_stop] * i_nf)) / sum(i_nf) \ - if sum(i_nf) > 0 else 0 - - return min(max(np.rint(nf).astype(int), 0), 7) + return min(max(np.rint(8 - 16 * l_nf).astype(int), 0), 7) def run(self, bw, nbytes, nbits_bw, nbits_ltpf, nbits_sns, nbits_tns, x): @@ -268,7 +265,7 @@ class SpectrumAnalysis(SpectrumQuantization): g_off = self.get_gain_offset(nbytes) - (g_int, self.reset_off) = \ + (g_min, g_int, self.reset_off) = \ self.estimate_gain(x, nbits_spec, nbits_off, g_off) self.nbits_off = nbits_off self.nbits_spec = nbits_spec @@ -285,6 +282,7 @@ class SpectrumAnalysis(SpectrumQuantization): ### Adjust gain and requantize g_adj = self.adjust_gain(g_int - g_off, nbits_est, nbits_spec) + g_adj = max(g_int + g_adj, g_min + g_off) - g_int (xg, xq, lastnz) = self.quantize(g_adj, xg) @@ -410,9 +408,9 @@ class SpectrumSynthesis(SpectrumQuantization): def fill_noise(self, bw, x, lastnz, f_nf, nf_seed): - (i_nf, nf_start, nf_stop) = self.get_noise_indices(bw, x, lastnz) + i_nf = self.get_noise_indices(bw, x, lastnz) - k_nf = nf_start + np.argwhere(i_nf) + k_nf = np.argwhere(i_nf) l_nf = (8 - f_nf)/16 for k in k_nf: @@ -578,6 +576,7 @@ def check_estimate_gain(rng, dt, sr): analysis = SpectrumAnalysis(dt, sr) + mismatch_count = 0 for i in range(10): x = rng.random(ne) * i * 1e2 @@ -586,14 +585,17 @@ def check_estimate_gain(rng, dt, sr): nbits_off = rng.random() * 10 g_off = 10 - int(rng.random() * 20) - (g_int, reset_off) = \ + (_, g_int, reset_off) = \ analysis.estimate_gain(x, nbits_budget, nbits_off, g_off) (g_int_c, reset_off_c, _) = lc3.spec_estimate_gain( dt, sr, x, nbits_budget, nbits_off, -g_off) - ok = ok and g_int_c == g_int - ok = ok and reset_off_c == reset_off + if g_int_c != g_int: + mismatch_count += 1 + + ok = ok and (g_int_c == g_int or mismatch_count <= 1) + ok = ok and (reset_off_c == reset_off or mismatch_count <= 1) return ok @@ -730,69 +732,71 @@ def check_noise(rng, dt, bw): def check_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K + ne = T.NE[dt][sr] ok = True state_c = initial_state() - for i in range(len(C.X_F[dt])): + for i in range(len(C.X_F[i0])): - g_int = lc3.spec_estimate_gain(dt, sr, C.X_F[dt][i], - C.NBITS_SPEC[dt][i], C.NBITS_OFFSET[dt][i], -C.GG_OFF[dt][i])[0] - ok = ok and g_int == C.GG_IND[dt][i] + C.GG_OFF[dt][i] + g_int = lc3.spec_estimate_gain(dt, sr, C.X_F[i0][i], + C.NBITS_SPEC[i0][i], C.NBITS_OFFSET[i0][i], -C.GG_OFF[i0][i])[0] + ok = ok and g_int == C.GG_IND[i0][i] + C.GG_OFF[i0][i] (_, xq, nq) = lc3.spec_quantize(dt, sr, - C.GG_IND[dt][i] + C.GG_OFF[dt][i], C.X_F[dt][i]) - ok = ok and np.any((xq - C.X_Q[dt][i]) == 0) - ok = ok and nq == C.LASTNZ[dt][i] + C.GG_IND[i0][i] + C.GG_OFF[i0][i], C.X_F[i0][i]) + ok = ok and np.any((xq - C.X_Q[i0][i]) == 0) + ok = ok and nq == C.LASTNZ[i0][i] nbits = lc3.spec_compute_nbits(dt, sr, - C.NBYTES[dt], C.X_Q[dt][i], C.LASTNZ[dt][i], 0)[0] - ok = ok and nbits == C.NBITS_EST[dt][i] + C.NBYTES[i0], C.X_Q[i0][i], C.LASTNZ[i0][i], 0)[0] + ok = ok and nbits == C.NBITS_EST[i0][i] g_adj = lc3.spec_adjust_gain(sr, - C.GG_IND[dt][i], C.NBITS_EST[dt][i], C.NBITS_SPEC[dt][i], 0) - ok = ok and g_adj == C.GG_IND_ADJ[dt][i] - C.GG_IND[dt][i] + C.GG_IND[i0][i], C.NBITS_EST[i0][i], C.NBITS_SPEC[i0][i], 0) + ok = ok and g_adj == C.GG_IND_ADJ[i0][i] - C.GG_IND[i0][i] - if C.GG_IND_ADJ[dt][i] != C.GG_IND[dt][i]: + if C.GG_IND_ADJ[i0][i] != C.GG_IND[i0][i]: (_, xq, nq) = lc3.spec_quantize(dt, sr, - C.GG_IND_ADJ[dt][i] + C.GG_OFF[dt][i], C.X_F[dt][i]) - lastnz = C.LASTNZ_REQ[dt][i] - ok = ok and np.any(((xq - C.X_Q_REQ[dt][i])[:lastnz]) == 0) + C.GG_IND_ADJ[i0][i] + C.GG_OFF[i0][i], C.X_F[i0][i]) + lastnz = C.LASTNZ_REQ[i0][i] + ok = ok and np.any(((xq - C.X_Q_REQ[i0][i])[:lastnz]) == 0) tns_data = { - 'nfilters' : C.NUM_TNS_FILTERS[dt][i], + 'nfilters' : C.NUM_TNS_FILTERS[i0][i], 'lpc_weighting' : [ True, True ], - 'rc_order' : [ C.RC_ORDER[dt][i][0], 0 ], - 'rc' : [ C.RC_I_1[dt][i] - 8, np.zeros(8, dtype = np.intc) ] + 'rc_order' : [ C.RC_ORDER[i0][i][0], 0 ], + 'rc' : [ C.RC_I_1[i0][i] - 8, np.zeros(8, dtype = np.intc) ] } - (x, xq, side) = lc3.spec_analyze(dt, sr, C.NBYTES[dt], - C.PITCH_PRESENT[dt][i], tns_data, state_c, C.X_F[dt][i]) + (x, xq, side) = lc3.spec_analyze(dt, sr, C.NBYTES[i0], + C.PITCH_PRESENT[i0][i], tns_data, state_c, C.X_F[i0][i]) - ok = ok and np.abs(state_c['nbits_off'] - C.NBITS_OFFSET[dt][i]) < 1e-5 - if C.GG_IND_ADJ[dt][i] != C.GG_IND[dt][i]: - xq = C.X_Q_REQ[dt][i] - nq = C.LASTNZ_REQ[dt][i] - ok = ok and side['g_idx'] == C.GG_IND_ADJ[dt][i] + ok = ok and np.abs(state_c['nbits_off'] - C.NBITS_OFFSET[i0][i]) < 1e-5 + if C.GG_IND_ADJ[i0][i] != C.GG_IND[i0][i]: + xq = C.X_Q_REQ[i0][i] + nq = C.LASTNZ_REQ[i0][i] + ok = ok and side['g_idx'] == C.GG_IND_ADJ[i0][i] ok = ok and side['nq'] == nq ok = ok and np.any(((xq[:nq] - xq[:nq])) == 0) else: - xq = C.X_Q[dt][i] - nq = C.LASTNZ[dt][i] - ok = ok and side['g_idx'] == C.GG_IND[dt][i] + xq = C.X_Q[i0][i] + nq = C.LASTNZ[i0][i] + ok = ok and side['g_idx'] == C.GG_IND[i0][i] ok = ok and side['nq'] == nq - ok = ok and np.any((xq[:nq] - C.X_Q[dt][i][:nq]) == 0) - ok = ok and side['lsb_mode'] == C.LSB_MODE[dt][i] + ok = ok and np.any((xq[:nq] - C.X_Q[i0][i][:nq]) == 0) + ok = ok and side['lsb_mode'] == C.LSB_MODE[i0][i] - gg = C.GG[dt][i] if C.GG_IND_ADJ[dt][i] == C.GG_IND[dt][i] \ - else C.GG_ADJ[dt][i] + gg = C.GG[i0][i] if C.GG_IND_ADJ[i0][i] == C.GG_IND[i0][i] \ + else C.GG_ADJ[i0][i] - nf = lc3.spec_estimate_noise(dt, C.P_BW[dt][i], - xq, nq, C.X_F[dt][i] / gg) - ok = ok and nf == C.F_NF[dt][i] + nf = lc3.spec_estimate_noise(dt, C.P_BW[i0][i], + xq, nq, C.X_F[i0][i] / gg) + ok = ok and nf == C.F_NF[i0][i] return ok @@ -810,7 +814,7 @@ def check(): ok = ok and check_unit(rng, dt, sr) ok = ok and check_noise(rng, dt, sr) - for dt in range(T.NUM_DT): + for dt in range(T.DT_7M5, T.NUM_DT): ok = ok and check_appendix_c(dt) return ok diff --git a/test/tables.py b/test/tables.py index 4886586..0d63593 100644 --- a/test/tables.py +++ b/test/tables.py @@ -19,11 +19,13 @@ import numpy as np ### Generic ### -DT_7M5 = 0 -DT_10M = 1 -NUM_DT = 2 +DT_2M5 = 0 +DT_5M = 1 +DT_7M5 = 2 +DT_10M = 3 +NUM_DT = 4 -DT_MS = np.array([ 7.5, 10 ]) +DT_MS = np.array([ 2.5, 5, 7.5, 10 ]) SRATE_8K = 0 @@ -35,14 +37,98 @@ NUM_SRATE = 5 SRATE_KHZ = np.array([ 8, 16, 24, 32, 48 ]) +NS = np.array([(SRATE_KHZ * DT_MS[dt]).astype(int) + for dt in range(NUM_DT) ]) -NS = [ (SRATE_KHZ * DT_MS[dt]).astype(int) for dt in range(NUM_DT) ] -NE = [ np.append(NS[dt][:-1], (NS[dt][-1] * 5) // 6) for dt in range(NUM_DT) ] +NE = np.array([np.append(NS[dt][:-1], (NS[dt][-1] * 5) // 6) + for dt in range(NUM_DT) ]) -ND = [ (23 * NS[0]) // 30, (5 * NS[1]) // 8 ] +ND = NS // 2 + np.array([ 5 * SRATE_KHZ, 5 * SRATE_KHZ, \ + 8 * SRATE_KHZ, 5 * SRATE_KHZ ]) // 4 +### LC3 PLUS - 5.9.1 ### -### 3.7.1/2 ### +I_2M5_8K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, +]) + +I_2M5_16K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 32, 34, 36, 38, 40, +]) + +I_2M5_24K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 28, 30, 32, + 34, 36, 38, 40, 42, 44, 47, 50, 53, 56, + 60, +]) + +I_2M5_32K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 26, 28, 30, 32, 34, + 36, 38, 40, 43, 46, 49, 52, 55, 59, 63, + 67, 71, 75, 80, +]) + +I_2M5_48K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 23, 25, 27, 29, 31, 33, 35, 37, + 40, 43, 46, 49, 52, 56, 60, 64, 68, 72, + 77, 82, 87, 93, 100, +]) + +I_5M_8K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, +]) + +I_5M_16K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, + 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, + 50, 52, 54, 57, 60, 63, 66, 69, 72, 76, + 80, +]) + +I_5M_24K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 23, 24, 26, 28, 30, 32, 34, + 36, 38, 40, 42, 44, 47, 50, 53, 56, 59, + 62, 65, 69, 73, 77, 81, 86, 91, 96, 101, + 107, 113, 120, +]) + +I_5M_32K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 22, 24, 26, 28, 30, 32, 34, 36, + 38, 40, 42, 45, 48, 51, 54, 57, 61, 65, + 69, 73, 78, 83, 88, 93, 99, 105, 112, 119, + 126, 134, 142, 151, 160, +]) + +I_5M_48K = np.array([ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, + 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20, 21, 23, 25, 27, 29, 31, 33, 35, 37, + 40, 43, 46, 49, 52, 55, 59, 63, 67, 72, + 77, 82, 87, 93, 99, 105, 112, 120, 128, 136, + 145, 155, 165, 176, 187, 200, +]) + +### LC3 - 3.7.1/2 ### I_10M_8K = np.array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, @@ -144,13 +230,1021 @@ I_7M5_48K = np.array([ 236, 251, 266, 283, 300 ]) -I = [ [ I_7M5_8K, I_7M5_16K, I_7M5_24K, I_7M5_32K, I_7M5_48K ], +I = [ [ I_2M5_8K, I_2M5_16K, I_2M5_24K, I_2M5_32K, I_2M5_48K ], + [ I_5M_8K , I_5M_16K , I_5M_24K , I_5M_32K , I_5M_48K ], + [ I_7M5_8K, I_7M5_16K, I_7M5_24K, I_7M5_32K, I_7M5_48K ], [ I_10M_8K, I_10M_16K, I_10M_24K, I_10M_32K, I_10M_48K ] ] +### LC3 PLUS - 5.9.2 ### -### 3.7.3 ### +W_2M5_8K = np.array([ + 6.73791440e-03, 2.73228958e-02, 6.16356097e-02, 1.11912504e-01, + 1.78705350e-01, 2.60752499e-01, 3.54977638e-01, 4.56769675e-01, + 5.60523927e-01, 6.60366535e-01, 7.50943422e-01, 8.28138232e-01, + 8.89585018e-01, 9.34874773e-01, 9.65405703e-01, 9.83902633e-01, + 9.93718088e-01, 9.98098731e-01, 9.99626637e-01, 9.99977291e-01, + 9.99977291e-01, 9.99626637e-01, 9.98098731e-01, 9.93718088e-01, + 9.83902633e-01, 9.65405703e-01, 9.34874773e-01, 8.89585018e-01, + 8.28138232e-01, 7.50943422e-01, 6.60366535e-01, 5.60523927e-01, + 4.56769675e-01, 3.54977638e-01, 2.60752499e-01, 1.78705350e-01, + 1.11912504e-01, 6.16356097e-02, 2.73228958e-02, 6.73791440e-03, +]) -W_10M_80 = np.array([ +W_2M5_16K = np.array([ + 4.76441615e-03, 1.20463628e-02, 2.22639654e-02, 3.58022311e-02, + 5.29905465e-02, 7.40851840e-02, 9.92538592e-02, 1.28563118e-01, + 1.61969244e-01, 1.99313241e-01, 2.40320282e-01, 2.84603818e-01, + 3.31674323e-01, 3.80952458e-01, 4.31786202e-01, 4.83471316e-01, + 5.35274327e-01, 5.86457098e-01, 6.36301916e-01, 6.84136019e-01, + 7.29354445e-01, 7.71440129e-01, 8.09980291e-01, 8.44678279e-01, + 8.75360204e-01, 9.01975984e-01, 9.24594627e-01, 9.43393949e-01, + 9.58645225e-01, 9.70693650e-01, 9.79935831e-01, 9.86795807e-01, + 9.91701328e-01, 9.95062145e-01, 9.97251917e-01, 9.98595014e-01, + 9.99358895e-01, 9.99752127e-01, 9.99927440e-01, 9.99988650e-01, + 9.99988650e-01, 9.99927440e-01, 9.99752127e-01, 9.99358895e-01, + 9.98595014e-01, 9.97251917e-01, 9.95062145e-01, 9.91701328e-01, + 9.86795807e-01, 9.79935831e-01, 9.70693650e-01, 9.58645225e-01, + 9.43393949e-01, 9.24594627e-01, 9.01975984e-01, 8.75360204e-01, + 8.44678279e-01, 8.09980291e-01, 7.71440129e-01, 7.29354445e-01, + 6.84136019e-01, 6.36301916e-01, 5.86457098e-01, 5.35274327e-01, + 4.83471316e-01, 4.31786202e-01, 3.80952458e-01, 3.31674323e-01, + 2.84603818e-01, 2.40320282e-01, 1.99313241e-01, 1.61969244e-01, + 1.28563118e-01, 9.92538592e-02, 7.40851840e-02, 5.29905465e-02, + 3.58022311e-02, 2.22639654e-02, 1.20463628e-02, 4.76441615e-03, +]) + +W_2M5_24K = np.array([ + 3.89013421e-03, 8.20259508e-03, 1.37023556e-02, 2.05296853e-02, + 2.88030773e-02, 3.86278514e-02, 5.00956972e-02, 6.32829654e-02, + 7.82488109e-02, 9.50334651e-02, 1.13656749e-01, 1.34116888e-01, + 1.56389671e-01, 1.80427981e-01, 2.06161727e-01, 2.33498181e-01, + 2.62322720e-01, 2.92499991e-01, 3.23875450e-01, 3.56277288e-01, + 3.89518694e-01, 4.23400416e-01, 4.57713594e-01, 4.92242792e-01, + 5.26769190e-01, 5.61073868e-01, 5.94941120e-01, 6.28161737e-01, + 6.60536189e-01, 6.91877646e-01, 7.22014766e-01, 7.50794208e-01, + 7.78082793e-01, 8.03769285e-01, 8.27765737e-01, 8.50008365e-01, + 8.70457945e-01, 8.89099694e-01, 9.05942652e-01, 9.21018560e-01, + 9.34380273e-01, 9.46099727e-01, 9.56265526e-01, 9.64980202e-01, + 9.72357239e-01, 9.78517931e-01, 9.83588198e-01, 9.87695434e-01, + 9.90965519e-01, 9.93520077e-01, 9.95474078e-01, 9.96933861e-01, + 9.97995624e-01, 9.98744422e-01, 9.99253666e-01, 9.99585105e-01, + 9.99789244e-01, 9.99906118e-01, 9.99966358e-01, 9.99992433e-01, + 9.99992433e-01, 9.99966358e-01, 9.99906118e-01, 9.99789244e-01, + 9.99585105e-01, 9.99253666e-01, 9.98744422e-01, 9.97995624e-01, + 9.96933861e-01, 9.95474078e-01, 9.93520077e-01, 9.90965519e-01, + 9.87695434e-01, 9.83588198e-01, 9.78517931e-01, 9.72357239e-01, + 9.64980202e-01, 9.56265526e-01, 9.46099727e-01, 9.34380273e-01, + 9.21018560e-01, 9.05942652e-01, 8.89099694e-01, 8.70457945e-01, + 8.50008365e-01, 8.27765737e-01, 8.03769285e-01, 7.78082793e-01, + 7.50794208e-01, 7.22014766e-01, 6.91877646e-01, 6.60536189e-01, + 6.28161737e-01, 5.94941120e-01, 5.61073868e-01, 5.26769190e-01, + 4.92242792e-01, 4.57713594e-01, 4.23400416e-01, 3.89518694e-01, + 3.56277288e-01, 3.23875450e-01, 2.92499991e-01, 2.62322720e-01, + 2.33498181e-01, 2.06161727e-01, 1.80427981e-01, 1.56389671e-01, + 1.34116888e-01, 1.13656749e-01, 9.50334651e-02, 7.82488109e-02, + 6.32829654e-02, 5.00956972e-02, 3.86278514e-02, 2.88030773e-02, + 2.05296853e-02, 1.37023556e-02, 8.20259508e-03, 3.89013421e-03, +]) + +W_2M5_32K = np.array([ + 3.36895835e-03, 6.45555741e-03, 1.01430808e-02, 1.45212685e-02, + 1.96507673e-02, 2.55835280e-02, 3.23662853e-02, 4.00411787e-02, + 4.86456418e-02, 5.82120708e-02, 6.87674290e-02, 8.03328498e-02, + 9.29232660e-02, 1.06547081e-01, 1.21205893e-01, 1.36894275e-01, + 1.53599623e-01, 1.71302066e-01, 1.89974455e-01, 2.09582413e-01, + 2.30084468e-01, 2.51432255e-01, 2.73570790e-01, 2.96438816e-01, + 3.19969212e-01, 3.44089476e-01, 3.68722258e-01, 3.93785956e-01, + 4.19195353e-01, 4.44862309e-01, 4.70696475e-01, 4.96606050e-01, + 5.22498546e-01, 5.48281575e-01, 5.73863636e-01, 5.99154896e-01, + 6.24067964e-01, 6.48518633e-01, 6.72426605e-01, 6.95716161e-01, + 7.18316799e-01, 7.40163807e-01, 7.61198780e-01, 7.81370064e-01, + 8.00633131e-01, 8.18950870e-01, 8.36293796e-01, 8.52640176e-01, + 8.67976054e-01, 8.82295205e-01, 8.95598976e-01, 9.07896060e-01, + 9.19202165e-01, 9.29539615e-01, 9.38936863e-01, 9.47427941e-01, + 9.55051846e-01, 9.61851871e-01, 9.67874900e-01, 9.73170662e-01, + 9.77790986e-01, 9.81789034e-01, 9.85218555e-01, 9.88133167e-01, + 9.90585664e-01, 9.92627388e-01, 9.94307658e-01, 9.95673273e-01, + 9.96768094e-01, 9.97632718e-01, 9.98304240e-01, 9.98816100e-01, + 9.99198030e-01, 9.99476075e-01, 9.99672688e-01, 9.99806905e-01, + 9.99894561e-01, 9.99948558e-01, 9.99979163e-01, 9.99994325e-01, + 9.99994325e-01, 9.99979163e-01, 9.99948558e-01, 9.99894561e-01, + 9.99806905e-01, 9.99672688e-01, 9.99476075e-01, 9.99198030e-01, + 9.98816100e-01, 9.98304240e-01, 9.97632718e-01, 9.96768094e-01, + 9.95673273e-01, 9.94307658e-01, 9.92627388e-01, 9.90585664e-01, + 9.88133167e-01, 9.85218555e-01, 9.81789034e-01, 9.77790986e-01, + 9.73170662e-01, 9.67874900e-01, 9.61851871e-01, 9.55051846e-01, + 9.47427941e-01, 9.38936863e-01, 9.29539615e-01, 9.19202165e-01, + 9.07896060e-01, 8.95598976e-01, 8.82295205e-01, 8.67976054e-01, + 8.52640176e-01, 8.36293796e-01, 8.18950870e-01, 8.00633131e-01, + 7.81370064e-01, 7.61198780e-01, 7.40163807e-01, 7.18316799e-01, + 6.95716161e-01, 6.72426605e-01, 6.48518633e-01, 6.24067964e-01, + 5.99154896e-01, 5.73863636e-01, 5.48281575e-01, 5.22498546e-01, + 4.96606050e-01, 4.70696475e-01, 4.44862309e-01, 4.19195353e-01, + 3.93785956e-01, 3.68722258e-01, 3.44089476e-01, 3.19969212e-01, + 2.96438816e-01, 2.73570790e-01, 2.51432255e-01, 2.30084468e-01, + 2.09582413e-01, 1.89974455e-01, 1.71302066e-01, 1.53599623e-01, + 1.36894275e-01, 1.21205893e-01, 1.06547081e-01, 9.29232660e-02, + 8.03328498e-02, 6.87674290e-02, 5.82120708e-02, 4.86456418e-02, + 4.00411787e-02, 3.23662853e-02, 2.55835280e-02, 1.96507673e-02, + 1.45212685e-02, 1.01430808e-02, 6.45555741e-03, 3.36895835e-03, +]) + +W_2M5_48K = np.array([ + 2.75074638e-03, 4.77524515e-03, 6.99126548e-03, 9.47011816e-03, + 1.22441576e-02, 1.53355947e-02, 1.87626677e-02, 2.25415434e-02, + 2.66870142e-02, 3.12127707e-02, 3.61315008e-02, 4.14549100e-02, + 4.71937052e-02, 5.33575588e-02, 5.99550649e-02, 6.69936920e-02, + 7.44797342e-02, 8.24182658e-02, 9.08130967e-02, 9.96667332e-02, + 1.08980342e-01, 1.18753719e-01, 1.28985265e-01, 1.39671964e-01, + 1.50809372e-01, 1.62391604e-01, 1.74411338e-01, 1.86859813e-01, + 1.99726844e-01, 2.13000841e-01, 2.26668825e-01, 2.40716465e-01, + 2.55128110e-01, 2.69886836e-01, 2.84974488e-01, 3.00371741e-01, + 3.16058154e-01, 3.32012241e-01, 3.48211540e-01, 3.64632687e-01, + 3.81251499e-01, 3.98043054e-01, 4.14981787e-01, 4.32041576e-01, + 4.49195839e-01, 4.66417630e-01, 4.83679744e-01, 5.00954812e-01, + 5.18215407e-01, 5.35434150e-01, 5.52583807e-01, 5.69637402e-01, + 5.86568309e-01, 6.03350364e-01, 6.19957957e-01, 6.36366131e-01, + 6.52550680e-01, 6.68488237e-01, 6.84156362e-01, 6.99533624e-01, + 7.14599684e-01, 7.29335364e-01, 7.43722715e-01, 7.57745082e-01, + 7.71387158e-01, 7.84635031e-01, 7.97476230e-01, 8.09899758e-01, + 8.21896119e-01, 8.33457339e-01, 8.44576978e-01, 8.55250134e-01, + 8.65473441e-01, 8.75245054e-01, 8.84564635e-01, 8.93433321e-01, + 9.01853689e-01, 9.09829718e-01, 9.17366735e-01, 9.24471360e-01, + 9.31151439e-01, 9.37415982e-01, 9.43275077e-01, 9.48739819e-01, + 9.53822215e-01, 9.58535102e-01, 9.62892048e-01, 9.66907259e-01, + 9.70595479e-01, 9.73971891e-01, 9.77052016e-01, 9.79851615e-01, + 9.82386589e-01, 9.84672882e-01, 9.86726389e-01, 9.88562863e-01, + 9.90197830e-01, 9.91646510e-01, 9.92923740e-01, 9.94043905e-01, + 9.95020875e-01, 9.95867954e-01, 9.96597827e-01, 9.97222527e-01, + 9.97753399e-01, 9.98201077e-01, 9.98575471e-01, 9.98885756e-01, + 9.99140376e-01, 9.99347044e-01, 9.99512763e-01, 9.99643838e-01, + 9.99745907e-01, 9.99823966e-01, 9.99882403e-01, 9.99925037e-01, + 9.99955157e-01, 9.99975561e-01, 9.99988598e-01, 9.99996217e-01, + 9.99996217e-01, 9.99988598e-01, 9.99975561e-01, 9.99955157e-01, + 9.99925037e-01, 9.99882403e-01, 9.99823966e-01, 9.99745907e-01, + 9.99643838e-01, 9.99512763e-01, 9.99347044e-01, 9.99140376e-01, + 9.98885756e-01, 9.98575471e-01, 9.98201077e-01, 9.97753399e-01, + 9.97222527e-01, 9.96597827e-01, 9.95867954e-01, 9.95020875e-01, + 9.94043905e-01, 9.92923740e-01, 9.91646510e-01, 9.90197830e-01, + 9.88562863e-01, 9.86726389e-01, 9.84672882e-01, 9.82386589e-01, + 9.79851615e-01, 9.77052016e-01, 9.73971891e-01, 9.70595479e-01, + 9.66907259e-01, 9.62892048e-01, 9.58535102e-01, 9.53822215e-01, + 9.48739819e-01, 9.43275077e-01, 9.37415982e-01, 9.31151439e-01, + 9.24471360e-01, 9.17366735e-01, 9.09829718e-01, 9.01853689e-01, + 8.93433321e-01, 8.84564635e-01, 8.75245054e-01, 8.65473441e-01, + 8.55250134e-01, 8.44576978e-01, 8.33457339e-01, 8.21896119e-01, + 8.09899758e-01, 7.97476230e-01, 7.84635031e-01, 7.71387158e-01, + 7.57745082e-01, 7.43722715e-01, 7.29335364e-01, 7.14599684e-01, + 6.99533624e-01, 6.84156362e-01, 6.68488237e-01, 6.52550680e-01, + 6.36366131e-01, 6.19957957e-01, 6.03350364e-01, 5.86568309e-01, + 5.69637402e-01, 5.52583807e-01, 5.35434150e-01, 5.18215407e-01, + 5.00954812e-01, 4.83679744e-01, 4.66417630e-01, 4.49195839e-01, + 4.32041576e-01, 4.14981787e-01, 3.98043054e-01, 3.81251499e-01, + 3.64632687e-01, 3.48211540e-01, 3.32012241e-01, 3.16058154e-01, + 3.00371741e-01, 2.84974488e-01, 2.69886836e-01, 2.55128110e-01, + 2.40716465e-01, 2.26668825e-01, 2.13000841e-01, 1.99726844e-01, + 1.86859813e-01, 1.74411338e-01, 1.62391604e-01, 1.50809372e-01, + 1.39671964e-01, 1.28985265e-01, 1.18753719e-01, 1.08980342e-01, + 9.96667332e-02, 9.08130967e-02, 8.24182658e-02, 7.44797342e-02, + 6.69936920e-02, 5.99550649e-02, 5.33575588e-02, 4.71937052e-02, + 4.14549100e-02, 3.61315008e-02, 3.12127707e-02, 2.66870142e-02, + 2.25415434e-02, 1.87626677e-02, 1.53355947e-02, 1.22441576e-02, + 9.47011816e-03, 6.99126548e-03, 4.77524515e-03, 2.75074638e-03, +]) + +W_5M_8K = np.array([ + 9.95908659e-04, 3.81905679e-03, 9.54083261e-03, 1.92165980e-02, + 3.38271908e-02, 5.42483167e-02, 8.12077767e-02, 1.15217189e-01, + 1.56494233e-01, 2.04936342e-01, 2.60116658e-01, 3.21281416e-01, + 3.87347300e-01, 4.56949708e-01, 5.28519296e-01, 6.00352249e-01, + 6.70689638e-01, 7.37804446e-01, 8.00092531e-01, 8.56140918e-01, + 9.04827229e-01, 9.45368503e-01, 9.77350743e-01, 1.00080087e+00, + 1.01617159e+00, 1.02431525e+00, 1.02641543e+00, 1.02385837e+00, + 1.01813571e+00, 1.01079482e+00, 1.00340651e+00, 9.96783127e-01, + 9.92099552e-01, 9.89220694e-01, 9.87965832e-01, 9.88127353e-01, + 9.89480554e-01, 9.91784992e-01, 9.94784758e-01, 9.98211967e-01, + 1.00179124e+00, 1.00524258e+00, 1.00828305e+00, 1.01063128e+00, + 1.01201530e+00, 1.01218075e+00, 1.01089677e+00, 1.00796336e+00, + 1.00322726e+00, 9.96605055e-01, 9.86828423e-01, 9.73125029e-01, + 9.54063648e-01, 9.28386428e-01, 8.95091686e-01, 8.53476936e-01, + 8.03209093e-01, 7.44473520e-01, 6.78078703e-01, 6.05397045e-01, + 5.28207751e-01, 4.48655296e-01, 3.69187599e-01, 2.92456641e-01, + 2.21071854e-01, 1.57314858e-01, 1.03052576e-01, 5.98273224e-02, + 2.87183192e-02, 9.68388493e-03, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, +]) + +W_5M_16K = np.array([ + 6.14338818e-04, 1.48958283e-03, 2.88410496e-03, 4.93429883e-03, + 7.77913046e-03, 1.15491061e-02, 1.63715562e-02, 2.23711616e-02, + 2.96615969e-02, 3.83566333e-02, 4.85561099e-02, 6.03505574e-02, + 7.38228820e-02, 8.90356369e-02, 1.06035623e-01, 1.24853486e-01, + 1.45493189e-01, 1.67943556e-01, 1.92172862e-01, 2.18123826e-01, + 2.45725974e-01, 2.74883943e-01, 3.05482471e-01, 3.37387380e-01, + 3.70441593e-01, 4.04474963e-01, 4.39300436e-01, 4.74722545e-01, + 5.10534149e-01, 5.46520192e-01, 5.82465810e-01, 6.18145266e-01, + 6.53341146e-01, 6.87836730e-01, 7.21417603e-01, 7.53888797e-01, + 7.85054657e-01, 8.14739745e-01, 8.42781936e-01, 8.69037674e-01, + 8.93393548e-01, 9.15748356e-01, 9.36027020e-01, 9.54173114e-01, + 9.70163547e-01, 9.84003644e-01, 9.95719942e-01, 1.00537427e+00, + 1.01304666e+00, 1.01884338e+00, 1.02289695e+00, 1.02535529e+00, + 1.02638288e+00, 1.02615553e+00, 1.02485397e+00, 1.02266460e+00, + 1.01977940e+00, 1.01639169e+00, 1.01269703e+00, 1.00888519e+00, + 1.00537874e+00, 1.00156378e+00, 9.98253156e-01, 9.95434664e-01, + 9.93095027e-01, 9.91217091e-01, 9.89780519e-01, 9.88762494e-01, + 9.88138324e-01, 9.87881941e-01, 9.87966213e-01, 9.88363051e-01, + 9.89043407e-01, 9.89977232e-01, 9.91133456e-01, 9.92480044e-01, + 9.93984121e-01, 9.95612147e-01, 9.97330059e-01, 9.99103363e-01, + 1.00089744e+00, 1.00267709e+00, 1.00440719e+00, 1.00605229e+00, + 1.00757693e+00, 1.00894586e+00, 1.01012424e+00, 1.01107797e+00, + 1.01177396e+00, 1.01218036e+00, 1.01226671e+00, 1.01200406e+00, + 1.01136522e+00, 1.01032500e+00, 1.00886073e+00, 1.00695298e+00, + 1.00458627e+00, 1.00174990e+00, 9.98438663e-01, 9.94650033e-01, + 9.89575685e-01, 9.83830313e-01, 9.76999916e-01, 9.68914116e-01, + 9.59403812e-01, 9.48308632e-01, 9.35486022e-01, 9.20810131e-01, + 9.04173226e-01, 8.85488225e-01, 8.64686495e-01, 8.41723747e-01, + 8.16587571e-01, 7.89298635e-01, 7.59917189e-01, 7.28547452e-01, + 6.95328294e-01, 6.60433402e-01, 6.24066143e-01, 5.86446142e-01, + 5.47816066e-01, 5.08449976e-01, 4.68636143e-01, 4.28678989e-01, + 3.88903272e-01, 3.49643142e-01, 3.11236082e-01, 2.74012847e-01, + 2.38284723e-01, 2.04337983e-01, 1.72430586e-01, 1.42793979e-01, + 1.15638588e-01, 9.11582177e-02, 6.95274904e-02, 5.08897541e-02, + 3.53343019e-02, 2.28668041e-02, 1.33800502e-02, 6.64050653e-03, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, +]) + +W_5M_24K = np.array([ + 5.08722763e-04, 9.95908659e-04, 1.68220801e-03, 2.60969726e-03, + 3.81905679e-03, 5.34931959e-03, 7.24390638e-03, 9.54083261e-03, + 1.22763764e-02, 1.54895024e-02, 1.92165980e-02, 2.34936962e-02, + 2.83519958e-02, 3.38271908e-02, 3.99493954e-02, 4.67477524e-02, + 5.42483167e-02, 6.24777078e-02, 7.14583592e-02, 8.12077767e-02, + 9.17440041e-02, 1.03076496e-01, 1.15217189e-01, 1.28166571e-01, + 1.41926438e-01, 1.56494233e-01, 1.71859319e-01, 1.88013425e-01, + 2.04936342e-01, 2.22612306e-01, 2.41015124e-01, 2.60116658e-01, + 2.79887101e-01, 3.00288014e-01, 3.21281416e-01, 3.42820846e-01, + 3.64859656e-01, 3.87347300e-01, 4.10229495e-01, 4.33449453e-01, + 4.56949708e-01, 4.80669640e-01, 5.04547382e-01, 5.28519296e-01, + 5.52519610e-01, 5.76487245e-01, 6.00352249e-01, 6.24050987e-01, + 6.47518259e-01, 6.70689638e-01, 6.93502907e-01, 7.15892752e-01, + 7.37804446e-01, 7.59178724e-01, 7.79958661e-01, 8.00092531e-01, + 8.19531865e-01, 8.38228896e-01, 8.56140918e-01, 8.73231695e-01, + 8.89470202e-01, 9.04827229e-01, 9.19273638e-01, 9.32794041e-01, + 9.45368503e-01, 9.56988393e-01, 9.67648642e-01, 9.77350743e-01, + 9.86102783e-01, 9.93912241e-01, 1.00080087e+00, 1.00678781e+00, + 1.01190127e+00, 1.01617159e+00, 1.01963641e+00, 1.02233661e+00, + 1.02431525e+00, 1.02562130e+00, 1.02630344e+00, 1.02641543e+00, + 1.02600793e+00, 1.02513744e+00, 1.02385837e+00, 1.02222694e+00, + 1.02030055e+00, 1.01813571e+00, 1.01579215e+00, 1.01332597e+00, + 1.01079482e+00, 1.00826513e+00, 1.00604687e+00, 1.00340651e+00, + 1.00097740e+00, 9.98770454e-01, 9.96783127e-01, 9.95011891e-01, + 9.93452397e-01, 9.92099552e-01, 9.90947600e-01, 9.89990243e-01, + 9.89220694e-01, 9.88631804e-01, 9.88216090e-01, 9.87965832e-01, + 9.87873077e-01, 9.87929693e-01, 9.88127353e-01, 9.88457554e-01, + 9.88911587e-01, 9.89480554e-01, 9.90155346e-01, 9.90926656e-01, + 9.91784992e-01, 9.92720684e-01, 9.93723921e-01, 9.94784758e-01, + 9.95893149e-01, 9.97038957e-01, 9.98211967e-01, 9.99402034e-01, + 1.00059832e+00, 1.00179124e+00, 1.00296984e+00, 1.00412379e+00, + 1.00524258e+00, 1.00631572e+00, 1.00733269e+00, 1.00828305e+00, + 1.00915642e+00, 1.00994254e+00, 1.01063128e+00, 1.01121274e+00, + 1.01167723e+00, 1.01201530e+00, 1.01221778e+00, 1.01227579e+00, + 1.01218075e+00, 1.01192443e+00, 1.01149892e+00, 1.01089677e+00, + 1.01011097e+00, 1.00913509e+00, 1.00796336e+00, 1.00659076e+00, + 1.00501312e+00, 1.00322726e+00, 1.00123106e+00, 9.99023556e-01, + 9.96605055e-01, 9.93989471e-01, 9.90453920e-01, 9.86828423e-01, + 9.82771674e-01, 9.78220667e-01, 9.73125029e-01, 9.67432353e-01, + 9.61094704e-01, 9.54063648e-01, 9.46295299e-01, 9.37748911e-01, + 9.28386428e-01, 9.18176261e-01, 9.07086156e-01, 8.95091686e-01, + 8.82169624e-01, 8.68302529e-01, 8.53476936e-01, 8.37685201e-01, + 8.20927526e-01, 8.03209093e-01, 7.84545048e-01, 7.64955485e-01, + 7.44473520e-01, 7.23134807e-01, 7.00986056e-01, 6.78078703e-01, + 6.54468651e-01, 6.30221215e-01, 6.05397045e-01, 5.80071577e-01, + 5.54312928e-01, 5.28207751e-01, 5.01836972e-01, 4.75290296e-01, + 4.48655296e-01, 4.22028112e-01, 3.95505797e-01, 3.69187599e-01, + 3.43173285e-01, 3.17563302e-01, 2.92456641e-01, 2.67946378e-01, + 2.44123133e-01, 2.21071854e-01, 1.98871915e-01, 1.77596763e-01, + 1.57314858e-01, 1.38090336e-01, 1.19983750e-01, 1.03052576e-01, + 8.73508501e-02, 7.29281158e-02, 5.98273224e-02, 4.80817884e-02, + 3.77113530e-02, 2.87183192e-02, 2.10835203e-02, 1.47628941e-02, + 9.68388493e-03, 5.64216879e-03, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, +]) + +W_5M_32K = np.array([ + 4.59588635e-04, 7.91932361e-04, 1.22792717e-03, 1.78365327e-03, + 2.47954941e-03, 3.32979945e-03, 4.35353548e-03, 5.56496516e-03, + 6.98610836e-03, 8.62988232e-03, 1.05134341e-02, 1.26508264e-02, + 1.50609045e-02, 1.77559123e-02, 2.07547598e-02, 2.40681372e-02, + 2.77120786e-02, 3.16993325e-02, 3.60460964e-02, 4.07612864e-02, + 4.58603812e-02, 5.13513668e-02, 5.72478022e-02, 6.35585474e-02, + 7.02945073e-02, 7.74598720e-02, 8.50663537e-02, 9.31164162e-02, + 1.01616296e-01, 1.10569081e-01, 1.19978929e-01, 1.29841729e-01, + 1.40162380e-01, 1.50937156e-01, 1.62163230e-01, 1.73835412e-01, + 1.85952036e-01, 1.98500883e-01, 2.11477855e-01, 2.24873256e-01, + 2.38676395e-01, 2.52872945e-01, 2.67454701e-01, 2.82403147e-01, + 2.97705015e-01, 3.13341912e-01, 3.29297670e-01, 3.45549016e-01, + 3.62079505e-01, 3.78864867e-01, 3.95885158e-01, 4.13114379e-01, + 4.30530830e-01, 4.48107672e-01, 4.65822779e-01, 4.83646639e-01, + 5.01556485e-01, 5.19522807e-01, 5.37519704e-01, 5.55518384e-01, + 5.73495781e-01, 5.91418665e-01, 6.09262289e-01, 6.26998116e-01, + 6.44600201e-01, 6.62038458e-01, 6.79290655e-01, 6.96325643e-01, + 7.13119439e-01, 7.29646991e-01, 7.45886459e-01, 7.61809472e-01, + 7.77395845e-01, 7.92620875e-01, 8.07466639e-01, 8.21910156e-01, + 8.35934316e-01, 8.49518047e-01, 8.62648584e-01, 8.75308323e-01, + 8.87488472e-01, 8.99173772e-01, 9.10352743e-01, 9.21014413e-01, + 9.31155619e-01, 9.40764474e-01, 9.49838224e-01, 9.58373260e-01, + 9.66369041e-01, 9.73823562e-01, 9.80744251e-01, 9.87129797e-01, + 9.92987227e-01, 9.98324140e-01, 1.00315076e+00, 1.00747371e+00, + 1.01130915e+00, 1.01466668e+00, 1.01756334e+00, 1.02001468e+00, + 1.02203987e+00, 1.02365426e+00, 1.02488162e+00, 1.02573929e+00, + 1.02625071e+00, 1.02643667e+00, 1.02632086e+00, 1.02592292e+00, + 1.02526998e+00, 1.02438219e+00, 1.02328494e+00, 1.02200083e+00, + 1.02055597e+00, 1.01897139e+00, 1.01727518e+00, 1.01548913e+00, + 1.01363936e+00, 1.01174775e+00, 1.00984084e+00, 1.00793976e+00, + 1.00640740e+00, 1.00437483e+00, 1.00246981e+00, 1.00068907e+00, + 9.99034600e-01, 9.97502490e-01, 9.96094155e-01, 9.94805124e-01, + 9.93636273e-01, 9.92582654e-01, 9.91644701e-01, 9.90817076e-01, + 9.90099845e-01, 9.89487369e-01, 9.88979432e-01, 9.88570179e-01, + 9.88259191e-01, 9.88040442e-01, 9.87913369e-01, 9.87871810e-01, + 9.87915076e-01, 9.88036894e-01, 9.88236456e-01, 9.88507369e-01, + 9.88848709e-01, 9.89253949e-01, 9.89722041e-01, 9.90246329e-01, + 9.90825634e-01, 9.91453181e-01, 9.92127681e-01, 9.92842250e-01, + 9.93595510e-01, 9.94380481e-01, 9.95195724e-01, 9.96034188e-01, + 9.96894383e-01, 9.97769201e-01, 9.98657113e-01, 9.99550974e-01, + 1.00044923e+00, 1.00134469e+00, 1.00223579e+00, 1.00311529e+00, + 1.00398160e+00, 1.00482747e+00, 1.00565128e+00, 1.00644577e+00, + 1.00720935e+00, 1.00793478e+00, 1.00862050e+00, 1.00925931e+00, + 1.00984974e+00, 1.01038469e+00, 1.01086278e+00, 1.01127704e+00, + 1.01162625e+00, 1.01190357e+00, 1.01210795e+00, 1.01223276e+00, + 1.01227709e+00, 1.01223451e+00, 1.01210432e+00, 1.01188029e+00, + 1.01156197e+00, 1.01114337e+00, 1.01062432e+00, 1.00999915e+00, + 1.00926803e+00, 1.00842570e+00, 1.00747277e+00, 1.00640448e+00, + 1.00522200e+00, 1.00392116e+00, 1.00250376e+00, 1.00096633e+00, + 9.99311401e-01, 9.97536270e-01, 9.95644231e-01, 9.93633392e-01, + 9.90867748e-01, 9.88232633e-01, 9.85362057e-01, 9.82230509e-01, + 9.78818585e-01, 9.75102633e-01, 9.71063185e-01, 9.66675967e-01, + 9.61924219e-01, 9.56784199e-01, 9.51239430e-01, 9.45270024e-01, + 9.38861570e-01, 9.31994644e-01, 9.24659203e-01, 9.16838340e-01, + 9.08521803e-01, 8.99696701e-01, 8.90356205e-01, 8.80487793e-01, + 8.70088421e-01, 8.59149213e-01, 8.47668639e-01, 8.35642897e-01, + 8.23075389e-01, 8.09964930e-01, 7.96320451e-01, 7.82146054e-01, + 7.67454182e-01, 7.52256346e-01, 7.36570205e-01, 7.20409055e-01, + 7.03797511e-01, 6.86754281e-01, 6.69304189e-01, 6.51471096e-01, + 6.33285483e-01, 6.14768539e-01, 5.95955378e-01, 5.76873796e-01, + 5.57553429e-01, 5.38032014e-01, 5.18345403e-01, 4.98525942e-01, + 4.78615607e-01, 4.58647304e-01, 4.38664366e-01, 4.18704689e-01, + 3.98812306e-01, 3.79026292e-01, 3.59391483e-01, 3.39947413e-01, + 3.20739242e-01, 3.01806111e-01, 2.83190595e-01, 2.64928837e-01, + 2.47060855e-01, 2.29620112e-01, 2.12643372e-01, 1.96160182e-01, + 1.80203520e-01, 1.64799688e-01, 1.49978755e-01, 1.35764352e-01, + 1.22184253e-01, 1.09260199e-01, 9.70178845e-02, 8.54768028e-02, + 7.46597638e-02, 6.45825432e-02, 5.52628119e-02, 4.67097698e-02, + 3.89324443e-02, 3.19297601e-02, 2.56981064e-02, 2.02225927e-02, + 1.54831778e-02, 1.14492491e-02, 8.07648266e-03, 5.30004408e-03, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, +]) + +W_5M_48K = np.array([ + 4.09010650e-04, 6.14338818e-04, 8.57175988e-04, 1.14701506e-03, + 1.48958283e-03, 1.88977038e-03, 2.35300080e-03, 2.88410496e-03, + 3.48821379e-03, 4.17004043e-03, 4.93429883e-03, 5.78707651e-03, + 6.73381174e-03, 7.77913046e-03, 8.92704496e-03, 1.01820289e-02, + 1.15491061e-02, 1.30334922e-02, 1.46395129e-02, 1.63715562e-02, + 1.82345538e-02, 2.02330949e-02, 2.23711616e-02, 2.46523735e-02, + 2.70810194e-02, 2.96615969e-02, 3.23988485e-02, 3.52960177e-02, + 3.83566333e-02, 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0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, +]) + +### LC3 - 3.7.3 ### + +W_7M5_8K = np.array([ + 2.95060859e-03, 7.17541132e-03, 1.37695374e-02, 2.30953556e-02, + 3.54036230e-02, 5.08289304e-02, 6.94696293e-02, 9.13884278e-02, + 1.16604575e-01, 1.45073546e-01, 1.76711174e-01, 2.11342953e-01, + 2.48768614e-01, 2.88701102e-01, 3.30823871e-01, 3.74814544e-01, + 4.20308013e-01, 4.66904918e-01, 5.14185341e-01, 5.61710041e-01, + 6.09026346e-01, 6.55671016e-01, 7.01218384e-01, 7.45240679e-01, + 7.87369206e-01, 8.27223833e-01, 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3.67502060e-01, 3.59680098e-01, + 3.51887312e-01, 3.44130166e-01, 3.36408100e-01, 3.28728966e-01, + 3.21090505e-01, 3.13496418e-01, 3.05951565e-01, 2.98454319e-01, + 2.91010565e-01, 2.83621109e-01, 2.76285415e-01, 2.69019585e-01, + 2.61812445e-01, 2.54659232e-01, 2.47584348e-01, 2.40578694e-01, + 2.33647009e-01, 2.26788433e-01, 2.20001992e-01, 2.13301325e-01, + 2.06677771e-01, 2.00140409e-01, 1.93683630e-01, 1.87310343e-01, + 1.81027384e-01, 1.74839476e-01, 1.68739644e-01, 1.62737273e-01, + 1.56825277e-01, 1.51012382e-01, 1.45298230e-01, 1.39687469e-01, + 1.34171842e-01, 1.28762544e-01, 1.23455562e-01, 1.18254662e-01, + 1.13159677e-01, 1.08171439e-01, 1.03290734e-01, 9.85202978e-02, + 9.38600023e-02, 8.93117360e-02, 8.48752103e-02, 8.05523737e-02, + 7.63429787e-02, 7.22489246e-02, 6.82699120e-02, 6.44077291e-02, + 6.06620003e-02, 5.70343711e-02, 5.35243715e-02, 5.01334690e-02, + 4.68610790e-02, 4.37084453e-02, 4.06748365e-02, 3.77612269e-02, + 3.49667099e-02, 3.22919275e-02, 2.97357669e-02, 2.72984629e-02, + 2.49787186e-02, 2.27762542e-02, 2.06895808e-02, 1.87178169e-02, + 1.68593418e-02, 1.51125125e-02, 1.34757094e-02, 1.19462709e-02, + 1.05228754e-02, 9.20130941e-03, 7.98124316e-03, 6.85547314e-03, + 5.82657334e-03, 4.87838525e-03, 4.02351119e-03, 3.15418663e-03, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, +]) + +W_10M_8K = np.array([ -7.07854671e-04, -2.09819773e-03, -4.52519808e-03, -8.23397633e-03, -1.33771310e-02, -1.99972156e-02, -2.80090946e-02, -3.72150208e-02, -4.73176826e-02, -5.79465483e-02, -6.86760675e-02, -7.90464744e-02, @@ -193,7 +1287,7 @@ W_10M_80 = np.array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ]) -W_10M_160 = np.array([ +W_10M_16K = np.array([ -4.61989875e-04, -9.74716672e-04, -1.66447310e-03, -2.59710692e-03, -3.80628516e-03, -5.32460872e-03, -7.17588528e-03, -9.38248086e-03, -1.19527030e-02, -1.48952816e-02, -1.82066640e-02, -2.18757093e-02, @@ -276,7 +1370,7 @@ W_10M_160 = np.array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ]) -W_10M_240 = np.array([ +W_10M_24K = np.array([ -3.61349642e-04, -7.07854671e-04, -1.07444364e-03, -1.53347854e-03, -2.09819773e-03, -2.77842087e-03, -3.58412992e-03, -4.52519808e-03, -5.60932724e-03, -6.84323454e-03, -8.23397633e-03, -9.78531476e-03, @@ -399,7 +1493,7 @@ W_10M_240 = np.array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ]) -W_10M_320 = np.array([ +W_10M_32K = np.array([ -3.02115349e-04, -5.86773749e-04, -8.36650400e-04, -1.12663536e-03, -1.47049294e-03, -1.87347339e-03, -2.33929236e-03, -2.87200807e-03, -3.47625639e-03, -4.15596382e-03, -4.91456379e-03, -5.75517250e-03, @@ -562,7 +1656,7 @@ W_10M_320 = np.array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ]) -W_10M_480 = np.array([ +W_10M_48K = np.array([ -2.35303215e-04, -4.61989875e-04, -6.26293154e-04, -7.92918043e-04, -9.74716672e-04, -1.18025689e-03, -1.40920904e-03, -1.66447310e-03, -1.94659161e-03, -2.25708173e-03, -2.59710692e-03, -2.96760762e-03, @@ -805,501 +1899,6 @@ W_10M_480 = np.array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ]) -W_7M5_60 = np.array([ - 2.95060859e-03, 7.17541132e-03, 1.37695374e-02, 2.30953556e-02, - 3.54036230e-02, 5.08289304e-02, 6.94696293e-02, 9.13884278e-02, - 1.16604575e-01, 1.45073546e-01, 1.76711174e-01, 2.11342953e-01, - 2.48768614e-01, 2.88701102e-01, 3.30823871e-01, 3.74814544e-01, - 4.20308013e-01, 4.66904918e-01, 5.14185341e-01, 5.61710041e-01, - 6.09026346e-01, 6.55671016e-01, 7.01218384e-01, 7.45240679e-01, - 7.87369206e-01, 8.27223833e-01, 8.64513675e-01, 8.98977415e-01, - 9.30407518e-01, 9.58599937e-01, 9.83447719e-01, 1.00488283e+00, - 1.02285381e+00, 1.03740495e+00, 1.04859791e+00, 1.05656184e+00, - 1.06149371e+00, 1.06362578e+00, 1.06325973e+00, 1.06074505e+00, - 1.05643590e+00, 1.05069500e+00, 1.04392435e+00, 1.03647725e+00, - 1.02872867e+00, 1.02106486e+00, 1.01400658e+00, 1.00727455e+00, - 1.00172250e+00, 9.97309592e-01, 9.93985158e-01, 9.91683335e-01, - 9.90325325e-01, 9.89822613e-01, 9.90074734e-01, 9.90975314e-01, - 9.92412851e-01, 9.94273149e-01, 9.96439157e-01, 9.98791616e-01, - 1.00120985e+00, 1.00357357e+00, 1.00575984e+00, 1.00764515e+00, - 1.00910687e+00, 1.01002476e+00, 1.01028203e+00, 1.00976919e+00, - 1.00838641e+00, 1.00605124e+00, 1.00269767e+00, 9.98280464e-01, - 9.92777987e-01, 9.86186892e-01, 9.77634164e-01, 9.67447270e-01, - 9.55129725e-01, 9.40389877e-01, 9.22959280e-01, 9.02607350e-01, - 8.79202689e-01, 8.52641750e-01, 8.22881272e-01, 7.89971715e-01, - 7.54030328e-01, 7.15255742e-01, 6.73936911e-01, 6.30414716e-01, - 5.85078858e-01, 5.38398518e-01, 4.90833753e-01, 4.42885823e-01, - 3.95091024e-01, 3.48004343e-01, 3.02196710e-01, 2.58227431e-01, - 2.16641416e-01, 1.77922122e-01, 1.42480547e-01, 1.10652194e-01, - 8.26995967e-02, 5.88334516e-02, 3.92030848e-02, 2.38629107e-02, - 1.26976223e-02, 5.35665361e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -]) - -W_7M5_120 = np.array([ - 2.20824874e-03, 3.81014420e-03, 5.91552473e-03, 8.58361457e-03, - 1.18759723e-02, 1.58335301e-02, 2.04918652e-02, 2.58883593e-02, - 3.20415894e-02, 3.89616721e-02, 4.66742169e-02, 5.51849337e-02, - 6.45038384e-02, 7.46411071e-02, 8.56000162e-02, 9.73846703e-02, - 1.09993603e-01, 1.23419277e-01, 1.37655457e-01, 1.52690437e-01, - 1.68513363e-01, 1.85093105e-01, 2.02410419e-01, 2.20450365e-01, - 2.39167941e-01, 2.58526168e-01, 2.78498539e-01, 2.99038432e-01, - 3.20104862e-01, 3.41658622e-01, 3.63660034e-01, 3.86062695e-01, - 4.08815272e-01, 4.31871046e-01, 4.55176988e-01, 4.78676593e-01, - 5.02324813e-01, 5.26060916e-01, 5.49831283e-01, 5.73576883e-01, - 5.97241338e-01, 6.20770242e-01, 6.44099662e-01, 6.67176382e-01, - 6.89958854e-01, 7.12379980e-01, 7.34396372e-01, 7.55966688e-01, - 7.77036981e-01, 7.97558114e-01, 8.17490856e-01, 8.36796950e-01, - 8.55447310e-01, 8.73400798e-01, 8.90635719e-01, 9.07128770e-01, - 9.22848784e-01, 9.37763323e-01, 9.51860206e-01, 9.65130600e-01, - 9.77556541e-01, 9.89126209e-01, 9.99846919e-01, 1.00970073e+00, - 1.01868229e+00, 1.02681455e+00, 1.03408981e+00, 1.04051196e+00, - 1.04610837e+00, 1.05088565e+00, 1.05486289e+00, 1.05807221e+00, - 1.06053414e+00, 1.06227662e+00, 1.06333815e+00, 1.06375557e+00, - 1.06356632e+00, 1.06282156e+00, 1.06155996e+00, 1.05981709e+00, - 1.05765876e+00, 1.05512006e+00, 1.05223985e+00, 1.04908779e+00, - 1.04569860e+00, 1.04210831e+00, 1.03838099e+00, 1.03455276e+00, - 1.03067200e+00, 1.02679167e+00, 1.02295558e+00, 1.01920733e+00, - 1.01587289e+00, 1.01221017e+00, 1.00884559e+00, 1.00577851e+00, - 1.00300262e+00, 1.00051460e+00, 9.98309229e-01, 9.96378601e-01, - 9.94718132e-01, 9.93316216e-01, 9.92166957e-01, 9.91258603e-01, - 9.90581104e-01, 9.90123118e-01, 9.89873712e-01, 9.89818707e-01, - 9.89946800e-01, 9.90243175e-01, 9.90695564e-01, 9.91288540e-01, - 9.92009469e-01, 9.92842693e-01, 9.93775067e-01, 9.94790398e-01, - 9.95875534e-01, 9.97014367e-01, 9.98192871e-01, 9.99394506e-01, - 1.00060586e+00, 1.00181040e+00, 1.00299457e+00, 1.00414155e+00, - 1.00523688e+00, 1.00626393e+00, 1.00720890e+00, 1.00805489e+00, - 1.00878802e+00, 1.00939182e+00, 1.00985296e+00, 1.01015529e+00, - 1.01028602e+00, 1.01022988e+00, 1.00997541e+00, 1.00950846e+00, - 1.00881848e+00, 1.00789488e+00, 1.00672876e+00, 1.00530991e+00, - 1.00363456e+00, 1.00169363e+00, 9.99485663e-01, 9.97006370e-01, - 9.94254687e-01, 9.91231967e-01, 9.87937115e-01, 9.84375125e-01, - 9.79890963e-01, 9.75269879e-01, 9.70180498e-01, 9.64580027e-01, - 9.58425534e-01, 9.51684014e-01, 9.44320232e-01, 9.36290624e-01, - 9.27580507e-01, 9.18153414e-01, 9.07976524e-01, 8.97050058e-01, - 8.85351360e-01, 8.72857927e-01, 8.59579819e-01, 8.45502615e-01, - 8.30619943e-01, 8.14946648e-01, 7.98489378e-01, 7.81262450e-01, - 7.63291769e-01, 7.44590843e-01, 7.25199287e-01, 7.05153668e-01, - 6.84490545e-01, 6.63245210e-01, 6.41477162e-01, 6.19235334e-01, - 5.96559133e-01, 5.73519989e-01, 5.50173851e-01, 5.26568538e-01, - 5.02781159e-01, 4.78860889e-01, 4.54877894e-01, 4.30898123e-01, - 4.06993964e-01, 3.83234031e-01, 3.59680098e-01, 3.36408100e-01, - 3.13496418e-01, 2.91010565e-01, 2.69019585e-01, 2.47584348e-01, - 2.26788433e-01, 2.06677771e-01, 1.87310343e-01, 1.68739644e-01, - 1.51012382e-01, 1.34171842e-01, 1.18254662e-01, 1.03290734e-01, - 8.93117360e-02, 7.63429787e-02, 6.44077291e-02, 5.35243715e-02, - 4.37084453e-02, 3.49667099e-02, 2.72984629e-02, 2.06895808e-02, - 1.51125125e-02, 1.05228754e-02, 6.85547314e-03, 4.02351119e-03, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -]) - -W_7M5_180 = np.array([ - 1.97084908e-03, 2.95060859e-03, 4.12447721e-03, 5.52688664e-03, - 7.17541132e-03, 9.08757730e-03, 1.12819105e-02, 1.37695374e-02, - 1.65600266e-02, 1.96650895e-02, 2.30953556e-02, 2.68612894e-02, - 3.09632560e-02, 3.54036230e-02, 4.01915610e-02, 4.53331403e-02, - 5.08289304e-02, 5.66815448e-02, 6.28935304e-02, 6.94696293e-02, - 7.64106314e-02, 8.37160016e-02, 9.13884278e-02, 9.94294008e-02, - 1.07834725e-01, 1.16604575e-01, 1.25736503e-01, 1.35226811e-01, - 1.45073546e-01, 1.55273819e-01, 1.65822194e-01, 1.76711174e-01, - 1.87928776e-01, 1.99473180e-01, 2.11342953e-01, 2.23524554e-01, - 2.36003100e-01, 2.48768614e-01, 2.61813811e-01, 2.75129161e-01, - 2.88701102e-01, 3.02514034e-01, 3.16558805e-01, 3.30823871e-01, - 3.45295567e-01, 3.59963992e-01, 3.74814544e-01, 3.89831817e-01, - 4.05001010e-01, 4.20308013e-01, 4.35739515e-01, 4.51277817e-01, - 4.66904918e-01, 4.82609041e-01, 4.98375466e-01, 5.14185341e-01, - 5.30021478e-01, 5.45869352e-01, 5.61710041e-01, 5.77528151e-01, - 5.93304696e-01, 6.09026346e-01, 6.24674189e-01, 6.40227555e-01, - 6.55671016e-01, 6.70995935e-01, 6.86184559e-01, 7.01218384e-01, - 7.16078449e-01, 7.30756084e-01, 7.45240679e-01, 7.59515122e-01, - 7.73561955e-01, 7.87369206e-01, 8.00923138e-01, 8.14211386e-01, - 8.27223833e-01, 8.39952374e-01, 8.52386102e-01, 8.64513675e-01, - 8.76324079e-01, 8.87814288e-01, 8.98977415e-01, 9.09803319e-01, - 9.20284312e-01, 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5.85078858e-01, 5.69649536e-01, 5.54084810e-01, - 5.38398518e-01, 5.22614738e-01, 5.06756805e-01, 4.90833753e-01, - 4.74866033e-01, 4.58876566e-01, 4.42885823e-01, 4.26906539e-01, - 4.10970973e-01, 3.95091024e-01, 3.79291327e-01, 3.63587417e-01, - 3.48004343e-01, 3.32563201e-01, 3.17287485e-01, 3.02196710e-01, - 2.87309403e-01, 2.72643992e-01, 2.58227431e-01, 2.44072856e-01, - 2.30208977e-01, 2.16641416e-01, 2.03398481e-01, 1.90486162e-01, - 1.77922122e-01, 1.65726674e-01, 1.53906397e-01, 1.42480547e-01, - 1.31453980e-01, 1.20841778e-01, 1.10652194e-01, 1.00891734e-01, - 9.15718851e-02, 8.26995967e-02, 7.42815529e-02, 6.63242382e-02, - 5.88334516e-02, 5.18140676e-02, 4.52698346e-02, 3.92030848e-02, - 3.36144159e-02, 2.85023308e-02, 2.38629107e-02, 1.96894227e-02, - 1.59720527e-02, 1.26976223e-02, 9.84937739e-03, 7.40724463e-03, - 5.35665361e-03, 3.83226552e-03, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -]) - -W_7M5_240 = np.array([ - 1.84833037e-03, 2.56481839e-03, 3.36762118e-03, 4.28736617e-03, - 5.33830143e-03, 6.52679223e-03, 7.86112587e-03, 9.34628179e-03, - 1.09916868e-02, 1.28011172e-02, 1.47805911e-02, 1.69307043e-02, - 1.92592307e-02, 2.17696937e-02, 2.44685983e-02, 2.73556543e-02, - 3.04319230e-02, 3.36980464e-02, 3.71583577e-02, 4.08148180e-02, - 4.46708068e-02, 4.87262995e-02, 5.29820633e-02, 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2.27762542e-02, 2.06895808e-02, 1.87178169e-02, - 1.68593418e-02, 1.51125125e-02, 1.34757094e-02, 1.19462709e-02, - 1.05228754e-02, 9.20130941e-03, 7.98124316e-03, 6.85547314e-03, - 5.82657334e-03, 4.87838525e-03, 4.02351119e-03, 3.15418663e-03, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -]) - ### 3.7.4 ### diff --git a/test/tns.py b/test/tns.py index d537590..92bbe1d 100644 --- a/test/tns.py +++ b/test/tns.py @@ -41,7 +41,25 @@ class Tns: SUB_LIM_7M5 = [ SUB_LIM_7M5_NB, SUB_LIM_7M5_WB, SUB_LIM_7M5_SSWB, SUB_LIM_7M5_SWB, SUB_LIM_7M5_FB ] - SUB_LIM = [ SUB_LIM_7M5, SUB_LIM_10M ] + SUB_LIM_5M_NB = [ [ 6, 23, 40 ] ] + SUB_LIM_5M_WB = [ [ 6, 43, 80 ] ] + SUB_LIM_5M_SSWB = [ [ 6, 63, 120 ] ] + SUB_LIM_5M_SWB = [ [ 6, 43, 80 ], [ 80, 120, 160 ] ] + SUB_LIM_5M_FB = [ [ 6, 53, 100 ], [ 100, 150, 200 ] ] + + SUB_LIM_5M = [ SUB_LIM_5M_NB, SUB_LIM_5M_WB, + SUB_LIM_5M_SSWB, SUB_LIM_5M_SWB, SUB_LIM_5M_FB ] + + SUB_LIM_2M5_NB = [ [ 3, 10, 20 ] ] + SUB_LIM_2M5_WB = [ [ 3, 20, 40 ] ] + SUB_LIM_2M5_SSWB = [ [ 3, 30, 60 ] ] + SUB_LIM_2M5_SWB = [ [ 3, 40, 80 ] ] + SUB_LIM_2M5_FB = [ [ 3, 50, 100 ] ] + + SUB_LIM_2M5 = [ SUB_LIM_2M5_NB, SUB_LIM_2M5_WB, + SUB_LIM_2M5_SSWB, SUB_LIM_2M5_SWB, SUB_LIM_2M5_FB ] + + SUB_LIM = [ SUB_LIM_2M5, SUB_LIM_5M, SUB_LIM_7M5, SUB_LIM_10M ] FREQ_LIM_10M_NB = [ 12, 80 ] FREQ_LIM_10M_WB = [ 12, 160 ] @@ -61,7 +79,25 @@ class Tns: FREQ_LIM_7M5 = [ FREQ_LIM_7M5_NB, FREQ_LIM_7M5_WB, FREQ_LIM_7M5_SSWB, FREQ_LIM_7M5_SWB, FREQ_LIM_7M5_FB ] - FREQ_LIM = [ FREQ_LIM_7M5, FREQ_LIM_10M ] + FREQ_LIM_5M_NB = [ 6, 40 ] + FREQ_LIM_5M_WB = [ 6, 80 ] + FREQ_LIM_5M_SSWB = [ 6, 120 ] + FREQ_LIM_5M_SWB = [ 6, 80, 160 ] + FREQ_LIM_5M_FB = [ 6, 100, 200 ] + + FREQ_LIM_5M = [ FREQ_LIM_5M_NB, FREQ_LIM_5M_WB, + FREQ_LIM_5M_SSWB, FREQ_LIM_5M_SWB, FREQ_LIM_5M_FB ] + + FREQ_LIM_2M5_NB = [ 3, 20 ] + FREQ_LIM_2M5_WB = [ 3, 40 ] + FREQ_LIM_2M5_SSWB = [ 3, 60 ] + FREQ_LIM_2M5_SWB = [ 3, 80 ] + FREQ_LIM_2M5_FB = [ 3, 100 ] + + FREQ_LIM_2M5 = [ FREQ_LIM_2M5_NB, FREQ_LIM_2M5_WB, + FREQ_LIM_2M5_SSWB, FREQ_LIM_2M5_SWB, FREQ_LIM_2M5_FB ] + + FREQ_LIM = [ FREQ_LIM_2M5, FREQ_LIM_5M, FREQ_LIM_7M5, FREQ_LIM_10M ] def __init__(self, dt): @@ -72,9 +108,11 @@ class Tns: def get_data(self): + rc = np.append(self.rc - 8, np.zeros((2, 8 - len(self.rc[0]))), axis=1) + return { 'nfilters' : self.nfilters, 'lpc_weighting' : self.lpc_weighting, - 'rc_order' : self.rc_order, 'rc' : self.rc - 8 } + 'rc_order' : self.rc_order, 'rc' : rc } def get_nbits(self): @@ -105,17 +143,18 @@ class TnsAnalysis(Tns): ### Normalized autocorrelation function S = Tns.SUB_LIM[self.dt][bw][f] + maxorder = [ 4, 8 ][self.dt > T.DT_5M] - r = np.append([ 3 ], np.zeros(8)) - e = [ sum(x[S[s]:S[s+1]] ** 2) for s in range(3) ] + r = np.append([ 3 ], np.zeros(maxorder)) + e = [ sum(x[S[s]:S[s+1]] ** 2) for s in range(len(S)-1) ] for k in range(len(r) if sum(e) > 0 else 0): c = [ np.dot(x[S[s]:S[s+1]-k], x[S[s]+k:S[s+1]]) - for s in range(3) ] + for s in range(len(S)-1) ] r[k] = np.sum( np.array(c) / np.array(e) ) - r *= np.exp(-0.5 * (0.02 * np.pi * np.arange(9)) ** 2) + r *= np.exp(-0.5 * (0.02 * np.pi * np.arange(1+maxorder)) ** 2) ### Levinson-Durbin recursion @@ -140,10 +179,10 @@ class TnsAnalysis(Tns): def coeffs_reflexion(self, a): - rc = np.zeros(8) + rc = np.zeros(len(a)-1) b = a.copy() - for k in range(8, 0, -1): + for k in range(len(rc), 0, -1): rc[k-1] = b[k] e = 1 - rc[k-1] ** 2 b[1:k] = (b[1:k] - rc[k-1] * b[k-1:0:-1]) / e @@ -186,9 +225,12 @@ class TnsAnalysis(Tns): y = x.copy() self.nfilters = len(Tns.SUB_LIM[self.dt][bw]) - self.lpc_weighting = nbytes * 8 < 48 * T.DT_MS[self.dt] + maxorder = [ 4, 8 ][self.dt > T.DT_5M] + + self.lpc_weighting = nbytes < 120 * (1 + self.dt) / 8 + self.rc_order = np.zeros(2, dtype=np.intc) - self.rc = np.zeros((2, 8), dtype=np.intc) + self.rc = np.zeros((2, maxorder), dtype=np.intc) for f in range(self.nfilters): @@ -258,7 +300,7 @@ class TnsSynthesis(Tns): def load(self, b, bw, nbytes): self.nfilters = len(Tns.SUB_LIM[self.dt][bw]) - self.lpc_weighting = nbytes * 8 < 48 * T.DT_MS[self.dt] + self.lpc_weighting = nbytes < 120 * (1 + self.dt) / 8 self.rc_order = np.zeros(2, dtype=np.intc) self.rc = 8 * np.ones((2, 8), dtype=np.intc) @@ -316,14 +358,14 @@ def check_analysis(rng, dt, bw): y = analysis.run(x, bw, nn_flag, nbytes) (y_c, data_c) = lc3.tns_analyze(dt, bw, nn_flag, nbytes, x) - ok = ok and data_c['nfilters'] == analysis.nfilters ok = ok and data_c['lpc_weighting'] == analysis.lpc_weighting + ok = ok and data_c['nfilters'] == analysis.nfilters for f in range(analysis.nfilters): rc_order = analysis.rc_order[f] rc_order_c = data_c['rc_order'][f] rc_c = 8 + data_c['rc'][f] ok = ok and rc_order_c == rc_order - ok = ok and not np.any((rc_c - analysis.rc[f])[:rc_order]) + ok = ok and not np.any(rc_c[:rc_order] - analysis.rc[f][:rc_order]) ok = ok and lc3.tns_get_nbits(data_c) == analysis.get_nbits() ok = ok and np.amax(np.abs(y_c - y)) < 1e-2 @@ -339,74 +381,78 @@ def check_synthesis(rng, dt, bw): x = rng.random(T.NE[dt][bw]) * 1e2 - synthesis.nfilters = 1 + int(bw >= T.SRATE_32K) - synthesis.rc_order = rng.integers(0, 9, 2) + maxorder = [ 4, 8 ][dt > T.DT_5M] + synthesis.nfilters = 1 + int(dt >= T.DT_5M and bw >= T.SRATE_32K) + synthesis.rc_order = rng.integers(0, 1+maxorder, 2) synthesis.rc = rng.integers(0, 17, 16).reshape(2, 8) y = synthesis.run(x, bw) y_c = lc3.tns_synthesize(dt, bw, synthesis.get_data(), x) - ok = ok and np.amax(np.abs(y_c - y) < 1e-6) + ok = ok and np.amax(np.abs(y_c - y) < 1e-4) return ok def check_analysis_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K + ok = True - fs = Tns.FREQ_LIM[dt][sr][0] - fe = Tns.FREQ_LIM[dt][sr][1] + fs = Tns.FREQ_LIM[i0][sr][0] + fe = Tns.FREQ_LIM[i0][sr][1] st = np.zeros(8) - for i in range(len(C.X_S[dt])): + for i in range(len(C.X_S[i0])): - (_, a) = lc3.tns_compute_lpc_coeffs(dt, sr, C.X_S[dt][i]) - ok = ok and np.amax(np.abs(a[0] - C.TNS_LEV_A[dt][i])) < 1e-5 + (_, a) = lc3.tns_compute_lpc_coeffs(dt, sr, C.X_S[i0][i]) + ok = ok and np.amax(np.abs(a[0] - C.TNS_LEV_A[i0][i])) < 1e-5 - rc = lc3.tns_lpc_reflection(a[0]) - ok = ok and np.amax(np.abs(rc - C.TNS_LEV_RC[dt][i])) < 1e-5 + rc = lc3.tns_lpc_reflection(dt, a[0]) + ok = ok and np.amax(np.abs(rc - C.TNS_LEV_RC[i0][i])) < 1e-5 - (rc_order, rc_i) = lc3.tns_quantize_rc(C.TNS_LEV_RC[dt][i]) - ok = ok and rc_order == C.RC_ORDER[dt][i][0] - ok = ok and np.any((rc_i + 8) - C.RC_I_1[dt][i] == 0) + (rc_order, rc_i) = lc3.tns_quantize_rc(dt, C.TNS_LEV_RC[i0][i]) + ok = ok and rc_order == C.RC_ORDER[i0][i][0] + ok = ok and np.any((rc_i + 8) - C.RC_I_1[i0][i] == 0) rc_q = lc3.tns_unquantize_rc(rc_i, rc_order) - ok = ok and np.amax(np.abs(rc_q - C.RC_Q_1[dt][i])) < 1e-6 + ok = ok and np.amax(np.abs(rc_q - C.RC_Q_1[i0][i])) < 1e-6 - (x, side) = lc3.tns_analyze(dt, sr, False, C.NBYTES[dt], C.X_S[dt][i]) + (x, side) = lc3.tns_analyze(dt, sr, False, C.NBYTES[i0], C.X_S[i0][i]) ok = ok and side['nfilters'] == 1 - ok = ok and side['rc_order'][0] == C.RC_ORDER[dt][i][0] - ok = ok and not np.any((side['rc'][0] + 8) - C.RC_I_1[dt][i]) - ok = ok and lc3.tns_get_nbits(side) == C.NBITS_TNS[dt][i] - ok = ok and np.amax(np.abs(x - C.X_F[dt][i])) < 1e-3 + ok = ok and side['rc_order'][0] == C.RC_ORDER[i0][i][0] + ok = ok and not np.any((side['rc'][0] + 8) - C.RC_I_1[i0][i]) + ok = ok and lc3.tns_get_nbits(side) == C.NBITS_TNS[i0][i] + ok = ok and np.amax(np.abs(x - C.X_F[i0][i])) < 1e-3 return ok def check_synthesis_appendix_c(dt): + i0 = dt - T.DT_7M5 sr = T.SRATE_16K + ok = True - for i in range(len(C.X_HAT_Q[dt])): + for i in range(len(C.X_HAT_Q[i0])): side = { 'nfilters' : 1, - 'lpc_weighting' : C.NBYTES[dt] * 8 < 48 * T.DT_MS[dt], - 'rc_order': C.RC_ORDER[dt][i], - 'rc': [ C.RC_I_1[dt][i] - 8, C.RC_I_2[dt][i] - 8 ] + 'lpc_weighting' : C.NBYTES[i0] < 120 * (1 + dt) / 8, + 'rc_order': C.RC_ORDER[i0][i], + 'rc': [ C.RC_I_1[i0][i] - 8, C.RC_I_2[i0][i] - 8 ] } - g_int = C.GG_IND_ADJ[dt][i] + C.GG_OFF[dt][i] - x = C.X_HAT_Q[dt][i] * (10 ** (g_int / 28)) + g_int = C.GG_IND_ADJ[i0][i] + C.GG_OFF[i0][i] + x = C.X_HAT_Q[i0][i] * (10 ** (g_int / 28)) x = lc3.tns_synthesize(dt, sr, side, x) - ok = ok and np.amax(np.abs(x - C.X_HAT_TNS[dt][i])) < 1e-3 - - if dt != T.DT_10M: - return ok + ok = ok and np.amax(np.abs(x - C.X_HAT_TNS[i0][i])) < 1e-3 sr = T.SRATE_48K + if dt != T.DT_10M: + return ok side = { 'nfilters' : 2, @@ -431,9 +477,9 @@ def check(): ok = ok and check_analysis(rng, dt, sr) ok = ok and check_synthesis(rng, dt, sr) - for dt in range(T.NUM_DT): - ok = ok and check_analysis_appendix_c(dt) - ok = ok and check_synthesis_appendix_c(dt) + for dt in range(T.DT_7M5, T.NUM_DT): + check_analysis_appendix_c(dt) + check_synthesis_appendix_c(dt) return ok diff --git a/test/tns_py.c b/test/tns_py.c index cb96d02..516bb71 100644 --- a/test/tns_py.c +++ b/test/tns_py.c @@ -36,13 +36,14 @@ static PyObject *compute_lpc_coeffs_py(PyObject *m, PyObject *args) CTYPES_CHECK("sr", (unsigned)bw < LC3_NUM_BANDWIDTH); int ne = LC3_NE(dt, bw); + int maxorder = dt <= LC3_DT_5M ? 4 : 8; CTYPES_CHECK("x", to_1d_ptr(x_obj, NPY_FLOAT, ne, &x)); g_obj = new_1d_ptr(NPY_FLOAT, 2, &g); a_obj = new_2d_ptr(NPY_FLOAT, 2, 9, &a); - compute_lpc_coeffs(dt, bw, x, g, a); + compute_lpc_coeffs(dt, bw, maxorder, x, g, a); return Py_BuildValue("NN", g_obj, a_obj); } @@ -50,15 +51,20 @@ static PyObject *compute_lpc_coeffs_py(PyObject *m, PyObject *args) static PyObject *lpc_reflection_py(PyObject *m, PyObject *args) { PyObject *a_obj, *rc_obj; + unsigned dt; float *a, *rc; - if (!PyArg_ParseTuple(args, "O", &a_obj)) + if (!PyArg_ParseTuple(args, "IO", &dt, &a_obj)) return NULL; - CTYPES_CHECK("a", to_1d_ptr(a_obj, NPY_FLOAT, 9, &a)); - rc_obj = new_1d_ptr(NPY_FLOAT, 8, &rc); + CTYPES_CHECK("dt", (unsigned)dt < LC3_NUM_DT); - lpc_reflection(a, rc); + int maxorder = dt <= LC3_DT_5M ? 4 : 8; + + CTYPES_CHECK("a", to_1d_ptr(a_obj, NPY_FLOAT, 9, &a)); + rc_obj = new_1d_ptr(NPY_FLOAT, maxorder, &rc); + + lpc_reflection(a, maxorder, rc); return Py_BuildValue("N", rc_obj); } @@ -66,17 +72,21 @@ static PyObject *lpc_reflection_py(PyObject *m, PyObject *args) static PyObject *quantize_rc_py(PyObject *m, PyObject *args) { PyObject *rc_obj, *rc_q_obj; + unsigned dt; float *rc; int rc_order, *rc_q; - if (!PyArg_ParseTuple(args, "O", &rc_obj)) + if (!PyArg_ParseTuple(args, "IO", &dt, &rc_obj)) return NULL; - CTYPES_CHECK("rc", to_1d_ptr(rc_obj, NPY_FLOAT, 8, &rc)); + CTYPES_CHECK("dt", (unsigned)dt < LC3_NUM_DT); + int maxorder = dt <= LC3_DT_5M ? 4 : 8; + + CTYPES_CHECK("rc", to_1d_ptr(rc_obj, NPY_FLOAT, 8, &rc)); rc_q_obj = new_1d_ptr(NPY_INT, 8, &rc_q); - quantize_rc(rc, &rc_order, rc_q); + quantize_rc(rc, maxorder, &rc_order, rc_q); return Py_BuildValue("iN", rc_order, rc_q_obj); } diff --git a/tools/lc3bin.c b/tools/lc3bin.c index 0eba59f..fd59447 100644 --- a/tools/lc3bin.c +++ b/tools/lc3bin.c @@ -33,7 +33,7 @@ struct lc3bin_header { uint16_t bitrate_100bps; uint16_t channels; uint16_t frame_10us; - uint16_t rfu; + uint16_t epmode; uint16_t nsamples_low; uint16_t nsamples_high; }; @@ -46,17 +46,26 @@ int lc3bin_read_header(FILE *fp, int *frame_us, int *srate_hz, int *nchannels, int *nsamples) { struct lc3bin_header hdr; + uint16_t hrmode = 0; if (fread(&hdr, sizeof(hdr), 1, fp) != 1 || hdr.file_id != LC3_FILE_ID || hdr.header_size < sizeof(hdr)) return -1; + int num_extended_params = (hdr.header_size - sizeof(hdr)) / sizeof(uint16_t); + if (num_extended_params >= 1 && + fread(&hrmode, sizeof(hrmode), 1, fp) != 1) + return -1; + *nchannels = hdr.channels; *frame_us = hdr.frame_10us * 10; *srate_hz = hdr.srate_100hz * 100; *nsamples = hdr.nsamples_low | (hdr.nsamples_high << 16); + if (hdr.epmode || hrmode) + return -1; + fseek(fp, hdr.header_size, SEEK_SET); return 0;