// on a segment
void featureMapping(MixtureServer & ms, FeatureServer & fs,Seg * seg,Config &config) {
unsigned long begin=seg->begin()+fs.getFirstFeatureIndexOfASource(seg->sourceName()); // Idx of the first frame of the current file in the feature server
fs.seekFeature(begin);
Feature f;
for (unsigned long idxFrame=0;idxFrame<seg->length();idxFrame++){ // for all the features of the segment
fs.readFeature(f,0);
featureMapping(ms,f,config);
fs.writeFeature(f);
}
}
// ----------------------------------------------------------------------------------------------------------
// Feature Warping giving a source(tab of histo, one by coeff) and a target distribution
// for a segment and cluster (segment is the minimum time unit to perform this)
void computeWarp(Histo *histoT,Histo &destH,FeatureServer & fs,unsigned long begin, unsigned long length,Config &config) {
unsigned long vectsize=fs.getVectSize(); // Get the vect size (number of coeff)
Feature f;
fs.seekFeature(begin);
for (unsigned long idxFrame=0;idxFrame<length;idxFrame++){ // for all the features of the segment
fs.readFeature(f,0);
// Get the feature;
for (unsigned int i = 0; i < vectsize; i++){ // For each coeff
f[i]=warping(f[i],histoT[i],destH); // Apply the warping function
}
fs.writeFeature(f);
}
}
// ----------------------------------------------------------------------------------------------------------
// Feature Mean subtraction and Cov reduction for a segment and cluster (segment is considerred to be the minimum time unit to perform this).
void computeZeroOne(const DoubleVector &featureMean,const DoubleVector &featureStd,FeatureServer & fs,unsigned long begin, unsigned long length,Config &config) {
unsigned long vectsize=fs.getVectSize(); // Get the vect size (number of coeff)
Feature f;
fs.seekFeature(begin);
for (unsigned long idxFrame=0;idxFrame<length;idxFrame++){ // for all the features of the segment
fs.readFeature(f,0);
// Get the feature;
for (unsigned int i = 0; i < vectsize; i++) { // For each coeff
f[i]=(f[i]-featureMean[i])/featureStd[i]; // Apply the 0 mean 1 cov normalisation
}
fs.writeFeature(f);
}
}
/// Normalize features with a smooth mixture transformation o't=ot-sum(P(c|ot)Uc.x)
void FactorAnalysisStat::normalizeFeatures(SegCluster &selectedSegments,FeatureServer &fs,Config & config){
if (verbose) cout << "(FactorAnalysisStat) Normalize Features" << endl;
MixtureGD & clientMixture=_ms.getMixtureGD(1); // copy the UBM mixture
unsigned long nt=0;
RealVector <double> m_xh_1; m_xh_1.setSize(_supervsize);
double *_m_xh_1=m_xh_1.getArray();
Seg *seg; // current selectd segment
selectedSegments.rewind();
String currentSource="";
while((seg=selectedSegments.getSeg())!=NULL){
unsigned long begin=seg->begin()+fs.getFirstFeatureIndexOfASource(seg->sourceName());
if (currentSource!=seg->sourceName()) {
currentSource=seg->sourceName();
this->getUX(m_xh_1,currentSource);
this->getSpeakerModel(clientMixture,currentSource);
if (verbose)cout << "Processing speaker["<<currentSource<<"]"<< endl;
}
fs.seekFeature(begin);
Feature f;
if (!_topGauss) {
for (unsigned long idxFrame=0;idxFrame<seg->length();idxFrame++){
fs.readFeature(f,0);
double *ff=f.getDataVector();
double sum=0.0;
RealVector <double> P;
P.setSize(_mixsize);
double *Prob=P.getArray();
for(unsigned long k=0;k<_mixsize;k++) {
Prob[k]=clientMixture.weight(k)*clientMixture.getDistrib(k).computeLK(f);
sum+=Prob[k];
}
for(unsigned long k=0;k<_mixsize;k++)
Prob[k]/=sum;
for(unsigned long k=0;k<_mixsize;k++) {
for (unsigned long i=0;i<_vsize;i++)
ff[i]-= Prob[k]*_m_xh_1[k*_vsize+i];
}
fs.writeFeature(f);
nt++;
}
}
else {
throw Exception("no topgauss yet",__FILE__,__LINE__);
}
}
};