FreeCalypso > hg > gsm-codec-lib
view libtwamr/vad2.c @ 473:2d46abdfbe91
libgsmefr version 1.1.0 for DHF addition
author | Mychaela Falconia <falcon@freecalypso.org> |
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date | Wed, 15 May 2024 05:22:02 +0000 |
parents | 0152c069d01f |
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/* ***************************************************************************** * * GSM AMR-NB speech codec R98 Version 7.6.0 December 12, 2001 * R99 Version 3.3.0 * REL-4 Version 4.1.0 * ***************************************************************************** * * File : vad2.c * Purpose : Voice Activity Detection (VAD) for AMR (option 2) * ***************************************************************************** */ /*************************************************************************** * * FUNCTION NAME: vad2() * * PURPOSE: * This function provides the Voice Activity Detection function option 2 * for the Adaptive Multi-rate (AMR) codec. * * INPUTS: * * farray_ptr * pointer to Word16[80] input array * vadState2 * pointer to vadState2 state structure * * OUTPUTS: * * state variables are updated * * RETURN VALUE: * * Word16 * VAD(m) - two successive calls to vad2() yield * the VAD decision for the 20 ms frame: * VAD_flag = VAD(m-1) || VAD(m) * * *************************************************************************/ /* Includes */ #include <stdint.h> #include <string.h> #include "tw_amr.h" #include "namespace.h" #include "typedef.h" #include "cnst.h" #include "basic_op.h" #include "oper_32b.h" #include "no_count.h" #include "log2.h" #include "pow2.h" #include "vad2.h" /* Local functions */ /*************************************************************************** * * FUNCTION NAME: fn10Log10 * * PURPOSE: * The purpose of this function is to take the 10*log base 10 of input and * divide by 128 and return; i.e. output = 10*log10(input)/128 (scaled as 7,8) * * INPUTS: * * L_Input * input (scaled as 31-fbits,fbits) * fbits * number of fractional bits on input * * OUTPUTS: * * none * * RETURN VALUE: * * Word16 * output (scaled as 7,8) * * DESCRIPTION: * * 10*log10(x)/128 = 10*(log10(2) * (log2(x<<fbits)-log2(1<<fbits)) >> 7 * = 3.0103 * (log2(x<<fbits) - fbits) >> 7 * = ((3.0103/4.0 * (log2(x<<fbits) - fbits) << 2) >> 7 * = (3.0103/4.0 * (log2(x<<fbits) - fbits) >> 5 * *************************************************************************/ static Word16 fn10Log10 (Word32 L_Input, Word16 fbits) { Word16 integer; /* Integer part of Log2. (range: 0<=val<=30) */ Word16 fraction; /* Fractional part of Log2. (range: 0<=val<1) */ Word32 Ltmp; Word16 tmp; Log2(L_Input, &integer, &fraction); integer = sub(integer, fbits); Ltmp = Mpy_32_16 (integer, fraction, 24660); /* 24660 = 10*log10(2)/4 scaled 0,15 */ Ltmp = L_shr_r(Ltmp, 5+1); /* extra shift for 30,1 => 15,0 extract correction */ tmp = extract_l(Ltmp); return (tmp); } /*************************************************************************** * * FUNCTION NAME: block_norm * * PURPOSE: * The purpose of this function is block normalise the input data sequence * * INPUTS: * * &in[0] * pointer to data sequence to be normalised * length * number of elements in data sequence * headroom * number of headroom bits (i.e., * * OUTPUTS: * * &out[0] * normalised output data sequence pointed to by &out[0] * * RETURN VALUE: * * Word16 * number of bits sequence was left shifted * * DESCRIPTION: * * 1) Search for maximum absolute valued data element * 2) Normalise the max element with "headroom" * 3) Transfer/shift the input sequence to the output buffer * 4) Return the number of left shifts * * CAVEATS: * An input sequence of all zeros will return the maximum * number of left shifts allowed, NOT the value returned * by a norm_s(0) call, since it desired to associate an * all zeros sequence with low energy. * *************************************************************************/ static Word16 block_norm (Word16 * in, Word16 * out, Word16 length, Word16 headroom) { Word16 i, max, scnt, adata; max = abs_s(in[0]); for (i = 1; i < length; i++) { adata = abs_s(in[i]); test(); if (sub(adata, max) > 0) { max = adata; move16(); } } test(); if (max != 0) { scnt = sub(norm_s(max), headroom); for (i = 0; i < length; i++) { out[i] = shl(in[i], scnt); move16(); } } else { scnt = sub(16, headroom); for (i = 0; i < length; i++) { out[i] = 0; move16(); } } return (scnt); } /********************************************* The VAD function ***************************************************/ Word16 vad2 (Word16 * farray_ptr, vadState2 * st) { /* * The channel table is defined below. In this table, the * lower and higher frequency coefficients for each of the 16 * channels are specified. The table excludes the coefficients * with numbers 0 (DC), 1, and 64 (Foldover frequency). */ static const Word16 ch_tbl[NUM_CHAN][2] = { {2, 3}, {4, 5}, {6, 7}, {8, 9}, {10, 11}, {12, 13}, {14, 16}, {17, 19}, {20, 22}, {23, 26}, {27, 30}, {31, 35}, {36, 41}, {42, 48}, {49, 55}, {56, 63} }; /* channel energy scaling table - allows efficient division by number * of DFT bins in the channel: 1/2, 1/3, 1/4, etc. */ static const Word16 ch_tbl_sh[NUM_CHAN] = { 16384, 16384, 16384, 16384, 16384, 16384, 10923, 10923, 10923, 8192, 8192, 6554, 5461, 4681, 4681, 4096 }; /* * The voice metric table is defined below. It is a non- * linear table with a deadband near zero. It maps the SNR * index (quantized SNR value) to a number that is a measure * of voice quality. */ static const Word16 vm_tbl[90] = { 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 10, 11, 12, 12, 13, 13, 14, 15, 15, 16, 17, 17, 18, 19, 20, 20, 21, 22, 23, 24, 24, 25, 26, 27, 28, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50 }; /* hangover as a function of peak SNR (3 dB steps) */ static const Word16 hangover_table[20] = { 30, 30, 30, 30, 30, 30, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 8, 8, 8 }; /* burst sensitivity as a function of peak SNR (3 dB steps) */ static const Word16 burstcount_table[20] = { 8, 8, 8, 8, 8, 8, 8, 8, 7, 6, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4 }; /* voice metric sensitivity as a function of peak SNR (3 dB steps) */ static const Word16 vm_threshold_table[20] = { 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 40, 51, 71, 100, 139, 191, 257, 337, 432 }; /* State tables that use 22,9 or 27,4 scaling for ch_enrg[] */ static const Word16 noise_floor_chan[2] = {NOISE_FLOOR_CHAN_0, NOISE_FLOOR_CHAN_1}; static const Word16 min_chan_enrg[2] = {MIN_CHAN_ENRG_0, MIN_CHAN_ENRG_1}; static const Word16 ine_noise[2] = {INE_NOISE_0, INE_NOISE_1}; static const Word16 fbits[2] = {FRACTIONAL_BITS_0, FRACTIONAL_BITS_1}; static const Word16 state_change_shift_r[2] = {STATE_1_TO_0_SHIFT_R, STATE_0_TO_1_SHIFT_R}; /* Energy scale table given 30,1 input scaling (also account for -6 dB shift on input) */ static const Word16 enrg_norm_shift[2] = {(FRACTIONAL_BITS_0-1+2), (FRACTIONAL_BITS_1-1+2)}; /* Automatic variables */ Word32 Lenrg; /* scaled as 30,1 */ Word32 Ltne; /* scaled as 22,9 */ Word32 Ltce; /* scaled as 22,9 or 27,4 */ Word16 tne_db; /* scaled as 7,8 */ Word16 tce_db; /* scaled as 7,8 */ Word16 input_buffer[FRM_LEN]; /* used for block normalising input data */ Word16 data_buffer[FFT_LEN]; /* used for in-place FFT */ Word16 ch_snr[NUM_CHAN]; /* scaled as 7,8 */ Word16 ch_snrq; /* scaled as 15,0 (in 0.375 dB steps) */ Word16 vm_sum; /* scaled as 15,0 */ Word16 ch_enrg_dev; /* scaled as 7,8 */ Word32 Lpeak; /* maximum channel energy */ Word16 p2a_flag; /* flag to indicate spectral peak-to-average ratio > 10 dB */ Word16 ch_enrg_db[NUM_CHAN]; /* scaled as 7,8 */ Word16 ch_noise_db; /* scaled as 7,8 */ Word16 alpha; /* scaled as 0,15 */ Word16 one_m_alpha; /* scaled as 0,15 */ Word16 update_flag; /* set to indicate a background noise estimate update */ Word16 i, j, j1, j2; /* Scratch variables */ Word16 hi1, lo1; Word32 Ltmp, Ltmp1, Ltmp2; Word16 tmp; Word16 normb_shift; /* block norm shift count */ Word16 ivad; /* intermediate VAD decision (return value) */ Word16 tsnrq; /* total signal-to-noise ratio (quantized 3 dB steps) scaled as 15,0 */ Word16 xt; /* instantaneous frame SNR in dB, scaled as 7,8 */ Word16 state_change; /* Increment frame counter */ st->Lframe_cnt = L_add(st->Lframe_cnt, 1); /* Block normalize the input */ normb_shift = block_norm(farray_ptr, input_buffer, FRM_LEN, FFT_HEADROOM); /* Pre-emphasize the input data and store in the data buffer with the appropriate offset */ for (i = 0; i < DELAY; i++) { data_buffer[i] = 0; move16(); } st->pre_emp_mem = shr_r(st->pre_emp_mem, sub(st->last_normb_shift, normb_shift)); st->last_normb_shift = normb_shift; move16(); data_buffer[DELAY] = add(input_buffer[0], mult(PRE_EMP_FAC, st->pre_emp_mem)); move16(); for (i = DELAY + 1, j = 1; i < DELAY + FRM_LEN; i++, j++) { data_buffer[i] = add(input_buffer[j], mult(PRE_EMP_FAC, input_buffer[j-1])); move16(); } st->pre_emp_mem = input_buffer[FRM_LEN-1]; move16(); for (i = DELAY + FRM_LEN; i < FFT_LEN; i++) { data_buffer[i] = 0; move16(); } /* Perform FFT on the data buffer */ r_fft(data_buffer); /* Use normb_shift factor to determine the scaling of the energy estimates */ state_change = 0; move16(); test(); if (st->shift_state == 0) { test(); if (sub(normb_shift, -FFT_HEADROOM+2) <= 0) { state_change = 1; move16(); st->shift_state = 1; move16(); } } else { test(); if (sub(normb_shift, -FFT_HEADROOM+5) >= 0) { state_change = 1; move16(); st->shift_state = 0; move16(); } } /* Scale channel energy estimate */ test(); if (state_change) { for (i = LO_CHAN; i <= HI_CHAN; i++) { st->Lch_enrg[i] = L_shr(st->Lch_enrg[i], state_change_shift_r[st->shift_state]); move32(); } } /* Estimate the energy in each channel */ test(); if (L_sub(st->Lframe_cnt, 1) == 0) { alpha = 32767; move16(); one_m_alpha = 0; move16(); } else { alpha = CEE_SM_FAC; move16(); one_m_alpha = ONE_MINUS_CEE_SM_FAC; move16(); } for (i = LO_CHAN; i <= HI_CHAN; i++) { Lenrg = 0; move16(); j1 = ch_tbl[i][0]; move16(); j2 = ch_tbl[i][1]; move16(); for (j = j1; j <= j2; j++) { Lenrg = L_mac(Lenrg, data_buffer[2 * j], data_buffer[2 * j]); Lenrg = L_mac(Lenrg, data_buffer[2 * j + 1], data_buffer[2 * j + 1]); } /* Denorm energy & scale 30,1 according to the state */ Lenrg = L_shr_r(Lenrg, sub(shl(normb_shift, 1), enrg_norm_shift[st->shift_state])); /* integrate over time: e[i] = (1-alpha)*e[i] + alpha*enrg/num_bins_in_chan */ tmp = mult(alpha, ch_tbl_sh[i]); L_Extract (Lenrg, &hi1, &lo1); Ltmp = Mpy_32_16(hi1, lo1, tmp); L_Extract (st->Lch_enrg[i], &hi1, &lo1); st->Lch_enrg[i] = L_add(Ltmp, Mpy_32_16(hi1, lo1, one_m_alpha)); move32(); test(); if (L_sub(st->Lch_enrg[i], min_chan_enrg[st->shift_state]) < 0) { st->Lch_enrg[i] = min_chan_enrg[st->shift_state]; move32(); } } /* Compute the total channel energy estimate (Ltce) */ Ltce = 0; move16(); for (i = LO_CHAN; i <= HI_CHAN; i++) { Ltce = L_add(Ltce, st->Lch_enrg[i]); } /* Calculate spectral peak-to-average ratio, set flag if p2a > 10 dB */ Lpeak = 0; move32(); for (i = LO_CHAN+2; i <= HI_CHAN; i++) /* Sine waves not valid for low frequencies */ { test(); if (L_sub(st->Lch_enrg [i], Lpeak) > 0) { Lpeak = st->Lch_enrg [i]; move32(); } } /* Set p2a_flag if peak (dB) > average channel energy (dB) + 10 dB */ /* Lpeak > Ltce/num_channels * 10^(10/10) */ /* Lpeak > (10/16)*Ltce */ L_Extract (Ltce, &hi1, &lo1); Ltmp = Mpy_32_16(hi1, lo1, 20480); test(); if (L_sub(Lpeak, Ltmp) > 0) { p2a_flag = TRUE; move16(); } else { p2a_flag = FALSE; move16(); } /* Initialize channel noise estimate to either the channel energy or fixed level */ /* Scale the energy appropriately to yield state 0 (22,9) scaling for noise */ test(); if (L_sub(st->Lframe_cnt, 4) <= 0) { test(); if (p2a_flag == TRUE) { for (i = LO_CHAN; i <= HI_CHAN; i++) { st->Lch_noise[i] = INE_NOISE_0; move32(); } } else { for (i = LO_CHAN; i <= HI_CHAN; i++) { test(); if (L_sub(st->Lch_enrg[i], ine_noise[st->shift_state]) < 0) { st->Lch_noise[i] = INE_NOISE_0; move32(); } else { test(); if (st->shift_state == 1) { st->Lch_noise[i] = L_shr(st->Lch_enrg[i], state_change_shift_r[0]); move32(); } else { st->Lch_noise[i] = st->Lch_enrg[i]; move32(); } } } } } /* Compute the channel energy (in dB), the channel SNRs, and the sum of voice metrics */ vm_sum = 0; move16(); for (i = LO_CHAN; i <= HI_CHAN; i++) { ch_enrg_db[i] = fn10Log10(st->Lch_enrg[i], fbits[st->shift_state]); move16(); ch_noise_db = fn10Log10(st->Lch_noise[i], FRACTIONAL_BITS_0); ch_snr[i] = sub(ch_enrg_db[i], ch_noise_db); move16(); /* quantize channel SNR in 3/8 dB steps (scaled 7,8 => 15,0) */ /* ch_snr = round((snr/(3/8))>>8) */ /* = round(((0.6667*snr)<<2)>>8) */ /* = round((0.6667*snr)>>6) */ ch_snrq = shr_r(mult(21845, ch_snr[i]), 6); /* Accumulate the sum of voice metrics */ test(); if (sub(ch_snrq, 89) < 0) { test(); if (ch_snrq > 0) { j = ch_snrq; move16(); } else { j = 0; move16(); } } else { j = 89; move16(); } vm_sum = add(vm_sum, vm_tbl[j]); } /* Initialize NOMINAL peak voice energy and average noise energy, calculate instantaneous SNR */ test(),test(),logic16(); if (L_sub(st->Lframe_cnt, 4) <= 0 || st->fupdate_flag == TRUE) { /* tce_db = (96 - 22 - 10*log10(64) (due to FFT)) scaled as 7,8 */ tce_db = 14320; move16(); st->negSNRvar = 0; move16(); st->negSNRbias = 0; move16(); /* Compute the total noise estimate (Ltne) */ Ltne = 0; move32(); for (i = LO_CHAN; i <= HI_CHAN; i++) { Ltne = L_add(Ltne, st->Lch_noise[i]); } /* Get total noise in dB */ tne_db = fn10Log10(Ltne, FRACTIONAL_BITS_0); /* Initialise instantaneous and long-term peak signal-to-noise ratios */ xt = sub(tce_db, tne_db); st->tsnr = xt; move16(); } else { /* Calculate instantaneous frame signal-to-noise ratio */ /* xt = 10*log10( sum(2.^(ch_snr*0.1*log2(10)))/length(ch_snr) ) */ Ltmp1 = 0; move32(); for (i=LO_CHAN; i<=HI_CHAN; i++) { /* Ltmp2 = ch_snr[i] * 0.1 * log2(10); (ch_snr scaled as 7,8) */ Ltmp2 = L_shr(L_mult(ch_snr[i], 10885), 8); L_Extract(Ltmp2, &hi1, &lo1); hi1 = add(hi1, 3); /* 2^3 to compensate for negative SNR */ Ltmp1 = L_add(Ltmp1, Pow2(hi1, lo1)); } xt = fn10Log10(Ltmp1, 4+3); /* average by 16, inverse compensation 2^3 */ /* Estimate long-term "peak" SNR */ test(),test(); if (sub(xt, st->tsnr) > 0) { /* tsnr = 0.9*tsnr + 0.1*xt; */ st->tsnr = round(L_add(L_mult(29491, st->tsnr), L_mult(3277, xt))); } /* else if (xt > 0.625*tsnr) */ else if (sub(xt, mult(20480, st->tsnr)) > 0) { /* tsnr = 0.998*tsnr + 0.002*xt; */ st->tsnr = round(L_add(L_mult(32702, st->tsnr), L_mult(66, xt))); } } /* Quantize the long-term SNR in 3 dB steps, limit to 0 <= tsnrq <= 19 */ tsnrq = shr(mult(st->tsnr, 10923), 8); /* tsnrq = min(19, max(0, tsnrq)); */ test(),test(); if (sub(tsnrq, 19) > 0) { tsnrq = 19; move16(); } else if (tsnrq < 0) { tsnrq = 0; move16(); } /* Calculate the negative SNR sensitivity bias */ test(); if (xt < 0) { /* negSNRvar = 0.99*negSNRvar + 0.01*xt*xt; */ /* xt scaled as 7,8 => xt*xt scaled as 14,17, shift to 7,8 and round */ tmp = round(L_shl(L_mult(xt, xt), 7)); st->negSNRvar = round(L_add(L_mult(32440, st->negSNRvar), L_mult(328, tmp))); /* if (negSNRvar > 4.0) negSNRvar = 4.0; */ test(); if (sub(st->negSNRvar, 1024) > 0) { st->negSNRvar = 1024; move16(); } /* negSNRbias = max(12.0*(negSNRvar - 0.65), 0.0); */ tmp = mult_r(shl(sub(st->negSNRvar, 166), 4), 24576); test(); if (tmp < 0) { st->negSNRbias = 0; move16(); } else { st->negSNRbias = shr(tmp, 8); } } /* Determine VAD as a function of the voice metric sum and quantized SNR */ tmp = add(vm_threshold_table[tsnrq], st->negSNRbias); test(); if (sub(vm_sum, tmp) > 0) { ivad = 1; move16(); st->burstcount = add(st->burstcount, 1); test(); if (sub(st->burstcount, burstcount_table[tsnrq]) > 0) { st->hangover = hangover_table[tsnrq]; move16(); } } else { st->burstcount = 0; move16(); st->hangover = sub(st->hangover, 1); test(); if (st->hangover <= 0) { ivad = 0; move16(); st->hangover = 0; move16(); } else { ivad = 1; move16(); } } /* Calculate log spectral deviation */ ch_enrg_dev = 0; move16(); test(); if (L_sub(st->Lframe_cnt, 1) == 0) { for (i = LO_CHAN; i <= HI_CHAN; i++) { st->ch_enrg_long_db[i] = ch_enrg_db[i]; move16(); } } else { for (i = LO_CHAN; i <= HI_CHAN; i++) { tmp = abs_s(sub(st->ch_enrg_long_db[i], ch_enrg_db[i])); ch_enrg_dev = add(ch_enrg_dev, tmp); } } /* * Calculate long term integration constant as a function of instantaneous SNR * (i.e., high SNR (tsnr dB) -> slower integration (alpha = HIGH_ALPHA), * low SNR (0 dB) -> faster integration (alpha = LOW_ALPHA) */ /* alpha = HIGH_ALPHA - ALPHA_RANGE * (tsnr - xt) / tsnr, low <= alpha <= high */ tmp = sub(st->tsnr, xt); test(),logic16(),test(),test(); if (tmp <= 0 || st->tsnr <= 0) { alpha = HIGH_ALPHA; move16(); one_m_alpha = 32768L-HIGH_ALPHA; move16(); } else if (sub(tmp, st->tsnr) > 0) { alpha = LOW_ALPHA; move16(); one_m_alpha = 32768L-LOW_ALPHA; move16(); } else { tmp = div_s(tmp, st->tsnr); alpha = sub(HIGH_ALPHA, mult(ALPHA_RANGE, tmp)); one_m_alpha = sub(32767, alpha); } /* Calc long term log spectral energy */ for (i = LO_CHAN; i <= HI_CHAN; i++) { Ltmp1 = L_mult(one_m_alpha, ch_enrg_db[i]); Ltmp2 = L_mult(alpha, st->ch_enrg_long_db[i]); st->ch_enrg_long_db[i] = round(L_add(Ltmp1, Ltmp2)); } /* Set or clear the noise update flags */ update_flag = FALSE; move16(); st->fupdate_flag = FALSE; move16(); test(),test(); if (sub(vm_sum, UPDATE_THLD) <= 0) { test(); if (st->burstcount == 0) { update_flag = TRUE; move16(); st->update_cnt = 0; move16(); } } else if (L_sub(Ltce, noise_floor_chan[st->shift_state]) > 0) { test(); if (sub(ch_enrg_dev, DEV_THLD) < 0) { test(); if (p2a_flag == FALSE) { test(); if (st->LTP_flag == FALSE) { st->update_cnt = add(st->update_cnt, 1); test(); if (sub(st->update_cnt, UPDATE_CNT_THLD) >= 0) { update_flag = TRUE; move16(); st->fupdate_flag = TRUE; move16(); } } } } } test(); if (sub(st->update_cnt, st->last_update_cnt) == 0) { st->hyster_cnt = add(st->hyster_cnt, 1); } else { st->hyster_cnt = 0; move16(); } st->last_update_cnt = st->update_cnt; move16(); test(); if (sub(st->hyster_cnt, HYSTER_CNT_THLD) > 0) { st->update_cnt = 0; move16(); } /* Conditionally update the channel noise estimates */ test(); if (update_flag == TRUE) { /* Check shift state */ test(); if (st->shift_state == 1) { /* get factor to shift ch_enrg[] from state 1 to 0 (noise always state 0) */ tmp = state_change_shift_r[0]; move16(); } else { /* No shift if already state 0 */ tmp = 0; move16(); } /* Update noise energy estimate */ for (i = LO_CHAN; i <= HI_CHAN; i++) { test(); /* integrate over time: en[i] = (1-alpha)*en[i] + alpha*e[n] */ /* (extract with shift compensation for state 1) */ L_Extract (L_shr(st->Lch_enrg[i], tmp), &hi1, &lo1); Ltmp = Mpy_32_16(hi1, lo1, CNE_SM_FAC); L_Extract (st->Lch_noise[i], &hi1, &lo1); st->Lch_noise[i] = L_add(Ltmp, Mpy_32_16(hi1, lo1, ONE_MINUS_CNE_SM_FAC)); move32(); /* Limit low level noise */ test(); if (L_sub(st->Lch_noise[i], MIN_NOISE_ENRG_0) < 0) { st->Lch_noise[i] = MIN_NOISE_ENRG_0; move32(); } } } return(ivad); } /* end of vad2 () */ /**** Other related functions *****/ /*************************************************************************** * * FUNCTION NAME: vad2_reset() * * PURPOSE: * The purpose of this function is to initialise the vad2() state * variables. * * INPUTS: * * &st * pointer to data structure of vad2 state variables * * OUTPUTS: * * none * * RETURN VALUE: * * none * * DESCRIPTION: * * Set all values in vad2 state to zero. Since it is * known that all elements in the structure contain * 16 and 32 bit fixed point elements, the initialisation * is performed by zeroing out the number of bytes in the * structure divided by two. * *************************************************************************/ void vad2_reset (vadState2 * st) { memset(st, 0, sizeof(vadState2)); } /* end of vad2_reset () */