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calculate all coefficients for several orders during cholesky factorization, the resulting coefficients are not strictly optimal though as there is a small difference in the autocorrelation matrixes which is ignored for the smaller orders
Originally committed as revision 5758 to svn://svn.ffmpeg.org/ffmpeg/trunk
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@ -742,35 +742,41 @@ static int lpc_calc_coefs(const int32_t *samples, int blocksize, int max_order,
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compute_autocorr(samples, blocksize, max_order+1, autoc);
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compute_lpc_coefs(autoc, max_order, lpc, ref);
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opt_order = estimate_best_order(ref, max_order);
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}else{
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LLSModel m[2];
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double var[MAX_LPC_ORDER+1], eval;
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double var[MAX_LPC_ORDER+1], eval, weight;
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for(pass=0; pass<use_lpc-1; 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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eval= av_evaluate_lls(&m[(pass-1)&1], var+1);
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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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for(j=0; j<=max_order; j++)
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var[j]/= sqrt(eval);
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}
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weight += 1/eval;
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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);
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opt_order= max_order; //FIXME
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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<opt_order; i++)
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lpc[opt_order-1][i]= m[(pass-1)&1].coeff[i];
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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 = estimate_best_order(ref, 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]);
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@ -49,12 +49,11 @@ void av_update_lls(LLSModel *m, double *var, double decay){
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}
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}
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double av_solve_lls(LLSModel *m, double threshold){
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void av_solve_lls(LLSModel *m, double threshold, int min_order){
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int i,j,k;
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double (*factor)[MAX_VARS+1]= &m->covariance[1][0];
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double (*covar )[MAX_VARS+1]= &m->covariance[1][1];
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double *covar_y = m->covariance[0];
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double variance;
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int count= m->indep_count;
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for(i=0; i<count; i++){
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@ -75,33 +74,34 @@ double av_solve_lls(LLSModel *m, double threshold){
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for(i=0; i<count; i++){
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double sum= covar_y[i+1];
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for(k=i-1; k>=0; k--)
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sum -= factor[i][k]*m->coeff[k];
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m->coeff[i]= sum / factor[i][i];
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sum -= factor[i][k]*m->coeff[0][k];
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m->coeff[0][i]= sum / factor[i][i];
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}
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for(i=count-1; i>=0; i--){
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double sum= m->coeff[i];
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for(k=i+1; k<count; k++)
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sum -= factor[k][i]*m->coeff[k];
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m->coeff[i]= sum / factor[i][i];
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}
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for(j=count-1; j>=min_order; j--){
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for(i=j; i>=0; i--){
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double sum= m->coeff[0][i];
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for(k=i+1; k<=j; k++)
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sum -= factor[k][i]*m->coeff[j][k];
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m->coeff[j][i]= sum / factor[i][i];
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}
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variance= covar_y[0];
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for(i=0; i<count; i++){
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double sum= m->coeff[i]*covar[i][i] - 2*covar_y[i+1];
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for(j=0; j<i; j++)
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sum += 2*m->coeff[j]*covar[j][i];
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variance += m->coeff[i]*sum;
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m->variance[j]= covar_y[0];
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for(i=0; i<=j; i++){
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double sum= m->coeff[j][i]*covar[i][i] - 2*covar_y[i+1];
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for(k=0; k<i; k++)
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sum += 2*m->coeff[j][k]*covar[k][i];
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m->variance[j] += m->coeff[j][i]*sum;
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}
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}
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return variance;
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}
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double av_evaluate_lls(LLSModel *m, double *param){
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double av_evaluate_lls(LLSModel *m, double *param, int order){
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int i;
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double out= 0;
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for(i=0; i<m->indep_count; i++)
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out+= param[i]*m->coeff[i];
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for(i=0; i<=order; i++)
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out+= param[i]*m->coeff[order][i];
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return out;
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}
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@ -113,27 +113,35 @@ double av_evaluate_lls(LLSModel *m, double *param){
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int main(){
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LLSModel m;
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int i;
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int i, order;
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av_init_lls(&m, 3);
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for(i=0; i<100; i++){
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double var[4];
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double eval, variance;
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#if 0
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var[1] = rand() / (double)RAND_MAX;
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var[2] = rand() / (double)RAND_MAX;
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var[3] = rand() / (double)RAND_MAX;
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var[2]= var[1] + var[3];
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var[2]= var[1] + var[3]/2;
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var[0] = var[1] + var[2] + var[3] + var[1]*var[2]/100;
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eval= av_evaluate_lls(&m, var+1);
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#else
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var[0] = (rand() / (double)RAND_MAX - 0.5)*2;
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var[1] = var[0] + rand() / (double)RAND_MAX - 0.5;
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var[2] = var[1] + rand() / (double)RAND_MAX - 0.5;
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var[3] = var[2] + rand() / (double)RAND_MAX - 0.5;
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#endif
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av_update_lls(&m, var, 0.99);
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variance= av_solve_lls(&m, 0.001);
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av_log(NULL, AV_LOG_DEBUG, "real:%f pred:%f var:%f coeffs:%f %f %f\n",
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var[0], eval, sqrt(variance / (i+1)),
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m.coeff[0], m.coeff[1], m.coeff[2]);
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av_solve_lls(&m, 0.001, 0);
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for(order=0; order<3; order++){
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eval= av_evaluate_lls(&m, var+1, order);
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av_log(NULL, AV_LOG_DEBUG, "real:%f order:%d pred:%f var:%f coeffs:%f %f %f\n",
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var[0], order, eval, sqrt(m.variance[order] / (i+1)),
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m.coeff[order][0], m.coeff[order][1], m.coeff[order][2]);
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}
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}
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return 0;
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}
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@ -30,13 +30,14 @@
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*/
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typedef struct LLSModel{
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double covariance[MAX_VARS+1][MAX_VARS+1];
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double coeff[MAX_VARS];
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double coeff[MAX_VARS][MAX_VARS];
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double variance[MAX_VARS];
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int indep_count;
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}LLSModel;
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void av_init_lls(LLSModel *m, int indep_count);
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void av_update_lls(LLSModel *m, double *param, double decay);
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double av_solve_lls(LLSModel *m, double threshold);
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double av_evaluate_lls(LLSModel *m, double *param);
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void av_solve_lls(LLSModel *m, double threshold, int min_order);
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double av_evaluate_lls(LLSModel *m, double *param, int order);
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#endif
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