void amp_experiment(struct AEXP *aexp, MODEL *model, char *arg) {
int m,i;
memcpy(&aexp->model_uq[2], model, sizeof(MODEL));
if (strcmp(arg, "qn") == 0) {
add_quant_noise(aexp, model, 1, model->L, 1);
update_snr_calc(aexp, &aexp->model_uq[2], model);
}
/* print training samples that can be > train.txt for training VQ */
if (strcmp(arg, "train") == 0)
print_sparse_amp_error(aexp, model, 00.0);
/* VQ of amplitudes, no interpolation (ie 10ms rate) */
if (strcmp(arg, "vq") == 0) {
sparse_vq_amp(aexp, model);
vq_interp(aexp, model, 0);
update_snr_calc(aexp, &aexp->model_uq[1], model);
}
/* VQ of amplitudes, interpolation (ie 20ms rate) */
if (strcmp(arg, "vqi") == 0) {
sparse_vq_amp(aexp, model);
vq_interp(aexp, model, 1);
update_snr_calc(aexp, &aexp->model_uq[1], model);
}
/* gain/shape VQ of amplitudes, 10ms rate (doesn't work that well) */
if (strcmp(arg, "gsvq") == 0) {
gain_shape_sparse_vq_amp(aexp, model);
vq_interp(aexp, model, 0);
update_snr_calc(aexp, &aexp->model_uq[1], model);
}
if (strcmp(arg, "smooth") == 0) {
smooth_samples(aexp, model, 0);
update_snr_calc(aexp, &aexp->model_uq[2], model);
}
if (strcmp(arg, "smoothtrain") == 0) {
smooth_samples(aexp, model, 1);
//update_snr_calc(aexp, &aexp->model_uq[2], model);
}
if (strcmp(arg, "smoothvq") == 0) {
smooth_samples(aexp, model, 2);
update_snr_calc(aexp, &aexp->model_uq[2], model);
}
if (strcmp(arg, "smoothamp") == 0) {
smooth_amp(aexp, model);
update_snr_calc(aexp, &aexp->model_uq[2], model);
}
/* update states */
for(m=1; m<=model->L; m++)
aexp->A_prev[m] = model->A[m];
aexp->frames++;
for(i=0; i<3; i++)
aexp->model_uq[i] = aexp->model_uq[i+1];
}
void phase_experiment(struct PEXP *pexp, MODEL *model, char *arg) {
int m;
float before[MAX_AMP], best_Wo;
assert(pexp != NULL);
memcpy(before, &model->phi[0], sizeof(float)*MAX_AMP);
if (strcmp(arg,"q3") == 0) {
quant_phases(model, 1, model->L, 3);
update_snr_calc(pexp, model, before);
update_variance_calc(pexp, model, before);
}
if (strcmp(arg,"dec2") == 0) {
if ((pexp->frames % 2) != 0) {
predict_phases(pexp, model, 1, model->L);
update_snr_calc(pexp, model, before);
update_variance_calc(pexp, model, before);
}
}
if (strcmp(arg,"repeat") == 0) {
if ((pexp->frames % 2) != 0) {
repeat_phases(pexp, model);
update_snr_calc(pexp, model, before);
update_variance_calc(pexp, model, before);
}
}
if (strcmp(arg,"vq") == 0) {
sparse_vq_pred_error(pexp, model);
update_snr_calc(pexp, model, before);
update_variance_calc(pexp, model, before);
}
if (strcmp(arg,"pred") == 0)
predict_phases_state(pexp, model, 1, model->L);
if (strcmp(arg,"pred1k") == 0)
predict_phases(pexp, model, 1, model->L/4);
if (strcmp(arg,"smooth") == 0) {
smooth_phase(pexp, model,0);
update_snr_calc(pexp, model, before);
}
if (strcmp(arg,"smoothtrain") == 0)
smooth_phase(pexp, model,1);
if (strcmp(arg,"smoothvq") == 0) {
smooth_phase(pexp, model,2);
update_snr_calc(pexp, model, before);
}
if (strcmp(arg,"smooth2") == 0)
smooth_phase2(pexp, model);
if (strcmp(arg,"smooth3") == 0)
smooth_phase3(pexp, model);
if (strcmp(arg,"smooth4") == 0)
smooth_phase4(model);
if (strcmp(arg,"vqsmooth3") == 0) {
sparse_vq_pred_error(pexp, model);
smooth_phase3(pexp, model);
}
if (strcmp(arg,"cb1") == 0) {
cb_phase1(pexp, model);
update_snr_calc(pexp, model, before);
}
if (strcmp(arg,"top") == 0) {
//top_amp(pexp, model, 1, model->L/4, 4, 1);
//top_amp(pexp, model, model->L/4, model->L/3, 4, 1);
//top_amp(pexp, model, model->L/3+1, model->L/2, 4, 1);
//top_amp(pexp, model, model->L/2, model->L, 6, 1);
//rand_phases(model, model->L/2, 3*model->L/4);
//struct_phases(pexp, model, model->L/2, 3*model->L/4);
//update_snr_calc(pexp, model, before);
}
if (strcmp(arg,"pred23") == 0) {
predict_phases2(pexp, model, model->L/2, model->L);
update_snr_calc(pexp, model, before);
}
if (strcmp(arg,"struct23") == 0) {
struct_phases(pexp, model, model->L/2, 3*model->L/4 );
update_snr_calc(pexp, model, before);
}
if (strcmp(arg,"addnoise") == 0) {
int m;
float max;
max = 0;
for(m=1; m<=model->L; m++)
if (model->A[m] > max)
max = model->A[m];
max = 20.0*log10(max);
for(m=1; m<=model->L; m++)
if (20.0*log10(model->A[m]) < (max-20)) {
model->phi[m] += (PI/4)*(1.0 -2.0*rand()/RAND_MAX);
//printf("m %d\n", m);
}
}
/* normalise phases */
for(m=1; m<=model->L; m++)
model->phi[m] = atan2(sin(model->phi[m]), cos(model->phi[m]));
/* update states */
//best_Wo = refine_Wo(pexp, model, model->L/2, model->L);
pexp->phi1 += N*model->Wo;
for(m=1; m<=model->L; m++)
pexp->phi_prev[m] = model->phi[m];
pexp->Wo_prev = model->Wo;
pexp->frames++;
pexp->prev_model = *model;
}
