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/**
* 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;
}
}
/**
* Calculate 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,
enum AVLPCType lpc_type, int lpc_passes,
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 &&
lpc_type > AV_LPC_TYPE_FIXED);
if (lpc_type == AV_LPC_TYPE_LEVINSON) {
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 if (lpc_type == AV_LPC_TYPE_CHOLESKY) {
LLSModel m[2];
double var[MAX_LPC_ORDER+1], av_uninit(weight);
for(pass=0; pass<lpc_passes; 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;
}
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