float vorbis_lpc_from_curve(float *curve,float *lpc,lpc_lookup *l){
int n=l->ln;
int m=l->m;
float *work=alloca(sizeof(*work)*(n+n));
float fscale=.5f/n;
int i,j;
/* input is a real curve. make it complex-real */
/* This mixes phase, but the LPC generation doesn't care. */
for(i=0;i<n;i++){
work[i*2]=curve[i]*fscale;
work[i*2+1]=0;
}
work[n*2-1]=curve[n-1]*fscale;
n*=2;
drft_backward(&l->fft,work);
/* The autocorrelation will not be circular. Shift, else we lose
most of the power in the edges. */
for(i=0,j=n/2;i<n/2;){
float temp=work[i];
work[i++]=work[j];
work[j++]=temp;
}
/* we *could* shave speed here by skimping on the edges (thus
speeding up the autocorrelation in vorbis_lpc_from_data) but we
don't right now. */
return(vorbis_lpc_from_data(work,lpc,n,m));
}
int speex_preprocess(SpeexPreprocessState *st, short *x, float *echo)
{
int i;
int is_speech=1;
float mean_post=0;
float mean_prior=0;
int N = st->ps_size;
int N3 = 2*N - st->frame_size;
int N4 = st->frame_size - N3;
float scale=.5/N;
float *ps=st->ps;
float Zframe=0, Pframe;
preprocess_analysis(st, x);
update_noise_prob(st);
st->nb_preprocess++;
/* Noise estimation always updated for the 20 first times */
if (st->nb_adapt<10)
{
update_noise(st, ps, echo);
}
/* Deal with residual echo if provided */
if (echo)
for (i=1;i<N;i++)
st->echo_noise[i] = (.7*st->echo_noise[i] + .3* echo[i]);
/* Compute a posteriori SNR */
for (i=1;i<N;i++)
{
st->post[i] = ps[i]/(1+st->noise[i]+st->echo_noise[i]) - 1;
if (st->post[i]>100)
st->post[i]=100;
/*if (st->post[i]<0)
st->post[i]=0;*/
mean_post+=st->post[i];
}
mean_post /= N;
if (mean_post<0)
mean_post=0;
/* Special case for first frame */
if (st->nb_adapt==1)
for (i=1;i<N;i++)
st->old_ps[i] = ps[i];
/* Compute a priori SNR */
{
/* A priori update rate */
float gamma;
float min_gamma=0.12;
gamma = 1.0/st->nb_preprocess;
/*Make update rate smaller when there's no speech*/
#if 0
if (mean_post<3.5 && mean_prior < 1)
min_gamma *= (mean_post+.5);
else
min_gamma *= 4.;
#else
min_gamma = .1*fabs(mean_prior - mean_post)*fabs(mean_prior - mean_post);
if (min_gamma>.15)
min_gamma = .15;
if (min_gamma<.02)
min_gamma = .02;
#endif
/*min_gamma = .08;*/
/*if (gamma<min_gamma)*/
gamma=min_gamma;
for (i=1;i<N;i++)
{
/* A priori SNR update */
st->prior[i] = gamma*max(0.0,st->post[i]) +
(1-gamma)*st->gain[i]*st->gain[i]*st->old_ps[i]/(1+st->noise[i]+st->echo_noise[i]);
if (st->prior[i]>100)
st->prior[i]=100;
mean_prior+=st->prior[i];
}
}
mean_prior /= N;
#if 0
for (i=0;i<N;i++)
{
fprintf (stderr, "%f ", st->prior[i]);
}
fprintf (stderr, "\n");
#endif
/*fprintf (stderr, "%f %f\n", mean_prior,mean_post);*/
if (st->nb_preprocess>=20)
{
int do_update = 0;
float noise_ener=0, sig_ener=0;
/* If SNR is low (both a priori and a posteriori), update the noise estimate*/
/*if (mean_prior<.23 && mean_post < .5)*/
if (mean_prior<.23 && mean_post < .5)
do_update = 1;
for (i=1;i<N;i++)
{
noise_ener += st->noise[i];
sig_ener += ps[i];
}
if (noise_ener > 3*sig_ener)
do_update = 1;
/*do_update = 0;*/
if (do_update)
{
st->consec_noise++;
} else {
st->consec_noise=0;
}
}
if (st->vad_enabled)
is_speech = speex_compute_vad(st, ps, mean_prior, mean_post);
if (st->consec_noise>=3)
{
update_noise(st, st->old_ps, echo);
} else {
for (i=1;i<N-1;i++)
{
if (st->update_prob[i]<.5)
st->noise[i] = .90*st->noise[i] + .1*st->ps[i];
}
}
for (i=1;i<N;i++)
{
st->zeta[i] = .7*st->zeta[i] + .3*st->prior[i];
}
{
int freq_start = (int)(300.0*2*N/st->sampling_rate);
int freq_end = (int)(2000.0*2*N/st->sampling_rate);
for (i=freq_start;i<freq_end;i++)
{
Zframe += st->zeta[i];
}
}
Zframe /= N;
if (Zframe<ZMIN)
{
Pframe = 0;
} else {
if (Zframe > 1.5*st->Zlast)
{
Pframe = 1;
st->Zpeak = Zframe;
if (st->Zpeak > 10)
st->Zpeak = 10;
if (st->Zpeak < 1)
st->Zpeak = 1;
} else {
if (Zframe < st->Zpeak*ZMIN)
{
Pframe = 0;
} else if (Zframe > st->Zpeak*ZMAX)
{
Pframe = 1;
} else {
Pframe = log(Zframe/(st->Zpeak*ZMIN)) / log(ZMAX/ZMIN);
}
}
}
st->Zlast = Zframe;
/*fprintf (stderr, "%f\n", Pframe);*/
/* Compute gain according to the Ephraim-Malah algorithm */
ephraim_malah(st,N,Pframe);
if (st->agc_enabled)
speex_compute_agc(st, mean_prior);
#if 0
if (!is_speech)
{
for (i=0;i<N;i++)
st->gain2[i] = 0;
}
#if 0
else {
for (i=0;i<N;i++)
st->gain2[i] = 1;
}
#endif
#endif
/* PERF: 14% when only vad is enabled [7.0 vs 8.2 sec] */
if(st->agc_enabled || st->denoise_enabled) {
/* Apply computed gain */
for (i=1;i<N;i++)
{
st->frame[2*i-1] *= st->gain2[i];
st->frame[2*i] *= st->gain2[i];
}
/* Get rid of the DC and very low frequencies */
st->frame[0]=0;
st->frame[1]=0;
st->frame[2]=0;
/* Nyquist frequency is mostly useless too */
st->frame[2*N-1]=0;
/* Inverse FFT with 1/N scaling */
drft_backward(st->fft_lookup, st->frame);
for (i=0;i<2*N;i++)
st->frame[i] *= scale;
{
float max_sample=0;
for (i=0;i<2*N;i++)
if (fabs(st->frame[i])>max_sample)
max_sample = fabs(st->frame[i]);
if (max_sample>28000)
{
float damp = 28000./max_sample;
for (i=0;i<2*N;i++)
st->frame[i] *= damp;
}
}
for (i=0;i<2*N;i++)
st->frame[i] *= st->window[i];
/* Perform overlap and add */
for (i=0;i<N3;i++)
x[i] = st->outbuf[i] + st->frame[i];
for (i=0;i<N4;i++)
x[N3+i] = st->frame[N3+i];
/* Update outbuf */
for (i=0;i<N3;i++)
st->outbuf[i] = st->frame[st->frame_size+i];
}
/* Save old power spectrum */
for (i=1;i<N;i++)
st->old_ps[i] = ps[i];
return is_speech;
}
