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