void MFCC::process(const MatrixXR& spectrum, MatrixXR* mfccCoeffs){
(*mfccCoeffs).resize(spectrum.rows(), _coefficientCount);
for ( int i = 0; i < spectrum.rows(); i++) {
LOUDIA_DEBUG("MFCC: Processing Melbands");
// Process the mel bands on the power of the spectrum
_melbands.process(spectrum.row(i).array().square(), &_bands);
LOUDIA_DEBUG("MFCC: Processing Log of bands");
// Apply a power to the log mel amplitudes as in: http://en.wikipedia.org/wiki/Mel_frequency_cepstral_coefficient
// V. Tyagi and C. Wellekens
// On desensitizing the Mel-Cepstrum to spurious spectral components for Robust Speech Recognition
// in Acoustics, Speech, and Signal Processing, 2005. Proceedings.
// IEEE International Conference on, vol. 1, 2005, pp. 529–532.
_bands = (_bands.array() + _minSpectrum).log() / log(10.0);
_bands = _bands.array().pow(_power);
LOUDIA_DEBUG("MFCC: Processing DCT");
// Process the DCT
_dct.process(_bands, &_coeffs);
(*mfccCoeffs).row(i) = _coeffs;
}
LOUDIA_DEBUG("MFCC: Finished Processing");
}
void SpectralNoiseSuppression::process(const MatrixXR& spectrum, MatrixXR* noise, MatrixXR* result){
const int rows = spectrum.rows();
const int cols = spectrum.cols();
(*result).resize(rows, cols);
(*result) = spectrum;
//DEBUG("SPECTRALNOISESUPPRESSION: Calculate wrapped magnitude.");
// Calculate the wrapped magnitude of the spectrum
_g = (1.0 / (_k1 - _k0 + 1.0) * spectrum.block(0, _k0, rows, _k1 - _k0).cwise().pow(1.0/3.0).rowwise().sum()).cwise().cube();
//cout << (_g) << endl;
for ( int i = 0; i < cols; i++ ) {
(*result).col(i) = (((*result).col(i).cwise() * _g.cwise().inverse()).cwise() + 1.0).cwise().log();
}
//cout << (*result) << endl;
//DEBUG("SPECTRALNOISESUPPRESSION: Estimate spectral noise.");
// Estimate spectral noise
_bands.process((*result), noise);
//DEBUG("SPECTRALNOISESUPPRESSION: Suppress spectral noise.");
// Suppress spectral noise
(*result) = ((*result) - (*noise)).cwise().clipUnder();
}
void Window::setWindow( const MatrixXR& window, bool callSetup ){
if (window.cols() != _inputSize || window.rows() != 1) {
// Throw exception wrong window size
}
setWindowType(CUSTOM, false);
_window = window;
if ( callSetup ) setup();
}
void PitchSaliency::process(const MatrixXR& spectrum, MatrixXR* pitches, MatrixXR* saliencies){
const int rows = spectrum.rows();
(*pitches).resize( rows, 1 );
(*saliencies).resize( rows, 1 );
for ( int row = 0; row < rows; row++ ) {
Real tLow = _tMin;
Real tUp = _tMax;
Real sal;
Real tLowBest = tLow;
Real tUpBest = tUp;
Real salBest;
Real period;
Real deltaPeriod;
while ( ( tUp - tLow ) > _tPrec ) {
// Split the best block and compute new limits
tLow = (tLowBest + tUpBest) / 2.0;
tUp = tUpBest;
tUpBest = tLow;
// Compute new saliences for the new blocks
period = (tLowBest + tUpBest) / 2.0;
deltaPeriod = tUpBest - tLowBest;
salBest = saliency(period, deltaPeriod, tLowBest, tUpBest, spectrum.row( row ));
period = (tLow + tUp) / 2.0;
deltaPeriod = tUp - tLow;
sal = saliency(period, deltaPeriod, tLow, tUp, spectrum.row( row ));
if (sal > salBest) {
tLowBest = tLow;
tUpBest = tUp;
salBest = sal;
}
}
period = (tLowBest + tUpBest) / 2.0;
deltaPeriod = tUpBest - tLowBest;
(*pitches)(row, 0) = _sampleRate / period;
(*saliencies)(row, 0) = saliency(period, deltaPeriod, tLowBest, tUpBest, spectrum.row( row ));
}
}
void VoiceActivityDetection::process(const MatrixXR& frames, MatrixXR* vad){
const int rows = frames.rows();
vad->resize(rows, 1);
for (int i=0; i < rows; i++){
// compute barkbands
_barkBands.process(frames.row(0), &_bands);
// copy frame into memory
_memory.row(_currentMemoryPos) = _bands.row(0);
_currentMemoryPos = (_currentMemoryPos + 1) % _memorySize;
// compute the VAD
RowXR LTSE = _memory.colwise().maxCoeff();
RowXR noise = _memory.colwise().sum() / _memorySize;
(*vad)(i,0) = log10((LTSE.array().square() / noise.array().square()).sum());
}
}
