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238 lines
6.8 KiB
238 lines
6.8 KiB
/** |
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* LPC utility code |
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* Copyright (c) 2006 Justin Ruggles <justin.ruggles@gmail.com> |
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* |
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* This file is part of FFmpeg. |
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* |
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* FFmpeg is free software; you can redistribute it and/or |
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* modify it under the terms of the GNU Lesser General Public |
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* License as published by the Free Software Foundation; either |
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* version 2.1 of the License, or (at your option) any later version. |
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* |
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* FFmpeg is distributed in the hope that it will be useful, |
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* but WITHOUT ANY WARRANTY; without even the implied warranty of |
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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* Lesser General Public License for more details. |
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* |
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* You should have received a copy of the GNU Lesser General Public |
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* License along with FFmpeg; if not, write to the Free Software |
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA |
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*/ |
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#include "libavutil/lls.h" |
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#include "dsputil.h" |
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#define LPC_USE_DOUBLE |
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#include "lpc.h" |
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/** |
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* Apply Welch window function to audio block |
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*/ |
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static void apply_welch_window(const int32_t *data, int len, double *w_data) |
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{ |
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int i, n2; |
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double w; |
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double c; |
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assert(!(len&1)); //the optimization in r11881 does not support odd len |
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//if someone wants odd len extend the change in r11881 |
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n2 = (len >> 1); |
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c = 2.0 / (len - 1.0); |
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w_data+=n2; |
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data+=n2; |
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for(i=0; i<n2; i++) { |
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w = c - n2 + i; |
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w = 1.0 - (w * w); |
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w_data[-i-1] = data[-i-1] * w; |
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w_data[+i ] = data[+i ] * w; |
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} |
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} |
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/** |
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* Calculate autocorrelation data from audio samples |
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* A Welch window function is applied before calculation. |
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*/ |
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void ff_lpc_compute_autocorr(const int32_t *data, int len, int lag, |
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double *autoc) |
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{ |
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int i, j; |
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double tmp[len + lag + 1]; |
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double *data1= tmp + lag; |
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apply_welch_window(data, len, data1); |
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for(j=0; j<lag; j++) |
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data1[j-lag]= 0.0; |
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data1[len] = 0.0; |
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for(j=0; j<lag; j+=2){ |
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double sum0 = 1.0, sum1 = 1.0; |
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for(i=j; i<len; i++){ |
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sum0 += data1[i] * data1[i-j]; |
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sum1 += data1[i] * data1[i-j-1]; |
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} |
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autoc[j ] = sum0; |
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autoc[j+1] = sum1; |
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} |
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if(j==lag){ |
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double sum = 1.0; |
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for(i=j-1; i<len; i+=2){ |
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sum += data1[i ] * data1[i-j ] |
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+ data1[i+1] * data1[i-j+1]; |
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} |
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autoc[j] = sum; |
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} |
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} |
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/** |
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* Quantize LPC coefficients |
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*/ |
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static void quantize_lpc_coefs(double *lpc_in, int order, int precision, |
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int32_t *lpc_out, int *shift, int max_shift, int zero_shift) |
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{ |
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int i; |
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double cmax, error; |
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int32_t qmax; |
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int sh; |
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/* define maximum levels */ |
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qmax = (1 << (precision - 1)) - 1; |
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/* find maximum coefficient value */ |
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cmax = 0.0; |
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for(i=0; i<order; i++) { |
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cmax= FFMAX(cmax, fabs(lpc_in[i])); |
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} |
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/* if maximum value quantizes to zero, return all zeros */ |
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if(cmax * (1 << max_shift) < 1.0) { |
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*shift = zero_shift; |
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memset(lpc_out, 0, sizeof(int32_t) * order); |
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return; |
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} |
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/* calculate level shift which scales max coeff to available bits */ |
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sh = max_shift; |
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while((cmax * (1 << sh) > qmax) && (sh > 0)) { |
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sh--; |
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} |
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/* since negative shift values are unsupported in decoder, scale down |
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coefficients instead */ |
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if(sh == 0 && cmax > qmax) { |
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double scale = ((double)qmax) / cmax; |
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for(i=0; i<order; i++) { |
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lpc_in[i] *= scale; |
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} |
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} |
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/* output quantized coefficients and level shift */ |
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error=0; |
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for(i=0; i<order; i++) { |
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error -= lpc_in[i] * (1 << sh); |
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lpc_out[i] = av_clip(lrintf(error), -qmax, qmax); |
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error -= lpc_out[i]; |
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} |
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*shift = sh; |
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} |
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static int estimate_best_order(double *ref, int min_order, int max_order) |
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{ |
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int i, est; |
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est = min_order; |
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for(i=max_order-1; i>=min_order-1; i--) { |
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if(ref[i] > 0.10) { |
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est = i+1; |
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break; |
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} |
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} |
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return est; |
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} |
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/** |
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* Calculate LPC coefficients for multiple orders |
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* |
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* @param use_lpc LPC method for determining coefficients |
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* 0 = LPC with fixed pre-defined coeffs |
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* 1 = LPC with coeffs determined by Levinson-Durbin recursion |
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* 2+ = LPC with coeffs determined by Cholesky factorization using (use_lpc-1) passes. |
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*/ |
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int ff_lpc_calc_coefs(DSPContext *s, |
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const int32_t *samples, int blocksize, int min_order, |
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int max_order, int precision, |
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int32_t coefs[][MAX_LPC_ORDER], int *shift, |
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enum AVLPCType lpc_type, int lpc_passes, |
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int omethod, int max_shift, int zero_shift) |
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{ |
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double autoc[MAX_LPC_ORDER+1]; |
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double ref[MAX_LPC_ORDER]; |
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double lpc[MAX_LPC_ORDER][MAX_LPC_ORDER]; |
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int i, j, pass; |
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int opt_order; |
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assert(max_order >= MIN_LPC_ORDER && max_order <= MAX_LPC_ORDER && |
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lpc_type > AV_LPC_TYPE_FIXED); |
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if (lpc_type == AV_LPC_TYPE_LEVINSON) { |
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s->lpc_compute_autocorr(samples, blocksize, max_order, autoc); |
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compute_lpc_coefs(autoc, max_order, &lpc[0][0], MAX_LPC_ORDER, 0, 1); |
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for(i=0; i<max_order; i++) |
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ref[i] = fabs(lpc[i][i]); |
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} else if (lpc_type == AV_LPC_TYPE_CHOLESKY) { |
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LLSModel m[2]; |
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double var[MAX_LPC_ORDER+1], av_uninit(weight); |
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for(pass=0; pass<lpc_passes; pass++){ |
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av_init_lls(&m[pass&1], max_order); |
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weight=0; |
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for(i=max_order; i<blocksize; i++){ |
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for(j=0; j<=max_order; j++) |
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var[j]= samples[i-j]; |
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if(pass){ |
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double eval, inv, rinv; |
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eval= av_evaluate_lls(&m[(pass-1)&1], var+1, max_order-1); |
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eval= (512>>pass) + fabs(eval - var[0]); |
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inv = 1/eval; |
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rinv = sqrt(inv); |
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for(j=0; j<=max_order; j++) |
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var[j] *= rinv; |
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weight += inv; |
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}else |
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weight++; |
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av_update_lls(&m[pass&1], var, 1.0); |
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} |
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av_solve_lls(&m[pass&1], 0.001, 0); |
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} |
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for(i=0; i<max_order; i++){ |
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for(j=0; j<max_order; j++) |
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lpc[i][j]=-m[(pass-1)&1].coeff[i][j]; |
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ref[i]= sqrt(m[(pass-1)&1].variance[i] / weight) * (blocksize - max_order) / 4000; |
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} |
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for(i=max_order-1; i>0; i--) |
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ref[i] = ref[i-1] - ref[i]; |
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} |
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opt_order = max_order; |
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if(omethod == ORDER_METHOD_EST) { |
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opt_order = estimate_best_order(ref, min_order, max_order); |
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i = opt_order-1; |
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quantize_lpc_coefs(lpc[i], i+1, precision, coefs[i], &shift[i], max_shift, zero_shift); |
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} else { |
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for(i=min_order-1; i<max_order; i++) { |
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quantize_lpc_coefs(lpc[i], i+1, precision, coefs[i], &shift[i], max_shift, zero_shift); |
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} |
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} |
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return opt_order; |
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}
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