autoComplexSpectrogram Sound_to_ComplexSpectrogram (Sound me, double windowLength, double timeStep) {
try {
double samplingFrequency = 1.0 / my dx, myDuration = my xmax - my xmin, t1;
if (windowLength > myDuration) {
Melder_throw (U"Your sound is too short:\nit should be at least as long as one window length.");
}
long nsamp_window = (long) floor (windowLength / my dx);
long halfnsamp_window = nsamp_window / 2 - 1;
nsamp_window = halfnsamp_window * 2;
if (nsamp_window < 2) {
Melder_throw (U"Your analysis window is too short: less than two samples.");
}
long numberOfFrames;
Sampled_shortTermAnalysis (me, windowLength, timeStep, &numberOfFrames, &t1);
// Compute sampling of the spectrum
long numberOfFrequencies = halfnsamp_window + 1;
double df = samplingFrequency / (numberOfFrequencies - 1);
autoComplexSpectrogram thee = ComplexSpectrogram_create (my xmin, my xmax, numberOfFrames, timeStep, t1, 0.0, 0.5 * samplingFrequency, numberOfFrequencies, df, 0.0);
//
autoSound analysisWindow = Sound_create (1, 0.0, nsamp_window * my dx, nsamp_window, my dx, 0.5 * my dx);
for (long iframe = 1; iframe <= numberOfFrames; iframe++) {
double t = Sampled_indexToX (thee.get(), iframe);
long leftSample = Sampled_xToLowIndex (me, t), rightSample = leftSample + 1;
long startSample = rightSample - halfnsamp_window;
long endSample = leftSample + halfnsamp_window;
Melder_assert (startSample >= 1);
Melder_assert (endSample <= my nx);
for (long j = 1; j <= nsamp_window; j++) {
analysisWindow -> z[1][j] = my z[1][startSample - 1 + j];
}
// window ?
autoSpectrum spec = Sound_to_Spectrum (analysisWindow.get(), 0);
thy z[1][iframe] = spec -> z[1][1] * spec -> z[1][1];
thy phase[1][iframe] = 0.0;
for (long ifreq = 2; ifreq <= numberOfFrequencies - 1; ifreq++) {
double x = spec -> z[1][ifreq], y = spec -> z[2][ifreq];
thy z[ifreq][iframe] = x * x + y * y; // power
thy phase[ifreq][iframe] = atan2 (y, x); // phase [-pi,+pi]
}
// even number of samples
thy z[numberOfFrequencies][iframe] = spec -> z[1][numberOfFrequencies] * spec -> z[1][numberOfFrequencies];
thy phase[numberOfFrequencies][iframe] = 0.0;
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": no ComplexSpectrogram created.");
}
}
static LPC _Sound_to_LPC (Sound me, int predictionOrder, double analysisWidth, double dt,
double preEmphasisFrequency, int method, double tol1, double tol2) {
double t1, samplingFrequency = 1.0 / my dx;
double windowDuration = 2 * analysisWidth; /* gaussian window */
long nFrames, frameErrorCount = 0;
if (floor (windowDuration / my dx) < predictionOrder + 1) Melder_throw ("Analysis window duration too short.\n"
"For a prediction order of ", predictionOrder, " the analysis window duration has to be greater than ", my dx * (predictionOrder + 1),
"Please increase the analysis window duration or lower the prediction order.");
// Convenience: analyse the whole sound into one LPC_frame
if (windowDuration > my dx * my nx) {
windowDuration = my dx * my nx;
}
Sampled_shortTermAnalysis (me, windowDuration, dt, & nFrames, & t1);
autoSound sound = Data_copy (me);
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoLPC thee = LPC_create (my xmin, my xmax, nFrames, dt, t1, predictionOrder, my dx);
autoMelderProgress progress (L"LPC analysis");
if (preEmphasisFrequency < samplingFrequency / 2) {
Sound_preEmphasis (sound.peek(), preEmphasisFrequency);
}
for (long i = 1; i <= nFrames; i++) {
LPC_Frame lpcframe = (LPC_Frame) & thy d_frames[i];
double t = Sampled_indexToX (thee.peek(), i);
LPC_Frame_init (lpcframe, predictionOrder);
Sound_into_Sound (sound.peek(), sframe.peek(), t - windowDuration / 2);
Vector_subtractMean (sframe.peek());
Sounds_multiply (sframe.peek(), window.peek());
if (method == LPC_METHOD_AUTO) {
if (! Sound_into_LPC_Frame_auto (sframe.peek(), lpcframe)) {
frameErrorCount++;
}
} else if (method == LPC_METHOD_COVAR) {
if (! Sound_into_LPC_Frame_covar (sframe.peek(), lpcframe)) {
frameErrorCount++;
}
} else if (method == LPC_METHOD_BURG) {
if (! Sound_into_LPC_Frame_burg (sframe.peek(), lpcframe)) {
frameErrorCount++;
}
} else if (method == LPC_METHOD_MARPLE) {
if (! Sound_into_LPC_Frame_marple (sframe.peek(), lpcframe, tol1, tol2)) {
frameErrorCount++;
}
}
if ( (i % 10) == 1) {
Melder_progress ( (double) i / nFrames, L"LPC analysis of frame ",
Melder_integer (i), L" out of ", Melder_integer (nFrames), L".");
}
}
return thee.transfer();
}
autoMelSpectrogram Sound_to_MelSpectrogram (Sound me, double analysisWidth, double dt, double f1_mel, double fmax_mel, double df_mel) {
try {
double t1, samplingFrequency = 1.0 / my dx, nyquist = 0.5 * samplingFrequency;
double windowDuration = 2.0 * analysisWidth; // gaussian window
double fmin_mel = 0.0;
double fbottom = NUMhertzToMel2 (100.0), fceiling = NUMhertzToMel2 (nyquist);
long numberOfFrames;
// Check defaults.
if (fmax_mel <= 0.0 || fmax_mel > fceiling) {
fmax_mel = fceiling;
}
if (fmax_mel <= f1_mel) {
f1_mel = fbottom; fmax_mel = fceiling;
}
if (f1_mel <= 0.0) {
f1_mel = fbottom;
}
if (df_mel <= 0.0) {
df_mel = 100.0;
}
// Determine the number of filters.
long numberOfFilters = lround ((fmax_mel - f1_mel) / df_mel);
fmax_mel = f1_mel + numberOfFilters * df_mel;
Sampled_shortTermAnalysis (me, windowDuration, dt, &numberOfFrames, &t1);
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoMelSpectrogram thee = MelSpectrogram_create (my xmin, my xmax, numberOfFrames, dt, t1, fmin_mel, fmax_mel, numberOfFilters, df_mel, f1_mel);
autoMelderProgress progress (U"MelSpectrograms analysis");
for (long iframe = 1; iframe <= numberOfFrames; iframe++) {
double t = Sampled_indexToX (thee.get(), iframe);
Sound_into_Sound (me, sframe.get(), t - windowDuration / 2.0);
Sounds_multiply (sframe.get(), window.get());
Sound_into_MelSpectrogram_frame (sframe.get(), thee.get(), iframe);
if (iframe % 10 == 1) {
Melder_progress ((double) iframe / numberOfFrames, U"Frame ", iframe, U" out of ", numberOfFrames, U".");
}
}
_Spectrogram_windowCorrection ((Spectrogram) thee.get(), window -> nx);
return thee;
} catch (MelderError) {
Melder_throw (me, U": no MelSpectrogram created.");
}
}
autoBarkSpectrogram Sound_to_BarkSpectrogram (Sound me, double analysisWidth, double dt, double f1_bark, double fmax_bark, double df_bark) {
try {
double nyquist = 0.5 / my dx, samplingFrequency = 2 * nyquist;
double windowDuration = 2 * analysisWidth; /* gaussian window */
double zmax = NUMhertzToBark2 (nyquist);
double fmin_bark = 0;
// Check defaults.
if (f1_bark <= 0) {
f1_bark = 1;
}
if (fmax_bark <= 0) {
fmax_bark = zmax;
}
if (df_bark <= 0) {
df_bark = 1;
}
fmax_bark = MIN (fmax_bark, zmax);
long numberOfFilters = lround ( (fmax_bark - f1_bark) / df_bark);
if (numberOfFilters <= 0) {
Melder_throw (U"The combination of filter parameters is not valid.");
}
long numberOfFrames; double t1;
Sampled_shortTermAnalysis (me, windowDuration, dt, & numberOfFrames, & t1);
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoBarkSpectrogram thee = BarkSpectrogram_create (my xmin, my xmax, numberOfFrames, dt, t1, fmin_bark, fmax_bark, numberOfFilters, df_bark, f1_bark);
autoMelderProgress progess (U"BarkSpectrogram analysis");
for (long iframe = 1; iframe <= numberOfFrames; iframe++) {
double t = Sampled_indexToX (thee.get(), iframe);
Sound_into_Sound (me, sframe.get(), t - windowDuration / 2.0);
Sounds_multiply (sframe.get(), window.get());
Sound_into_BarkSpectrogram_frame (sframe.get(), thee.get(), iframe);
if (iframe % 10 == 1) {
Melder_progress ( (double) iframe / numberOfFrames, U"BarkSpectrogram analysis: frame ",
iframe, U" from ", numberOfFrames, U".");
}
}
_Spectrogram_windowCorrection ((Spectrogram) thee.get(), window -> nx);
return thee;
} catch (MelderError) {
Melder_throw (me, U": no BarkSpectrogram created.");
}
}
PowerCepstrogram Sound_to_PowerCepstrogram (Sound me, double pitchFloor, double dt, double maximumFrequency, double preEmphasisFrequency) {
try {
// minimum analysis window has 3 periods of lowest pitch
double analysisWidth = 3 / pitchFloor;
double windowDuration = 2 * analysisWidth; /* gaussian window */
long nFrames;
// Convenience: analyse the whole sound into one Cepstrogram_frame
if (windowDuration > my dx * my nx) {
windowDuration = my dx * my nx;
}
double t1, samplingFrequency = 2 * maximumFrequency;
autoSound sound = Sound_resample (me, samplingFrequency, 50);
Sound_preEmphasis (sound.peek(), preEmphasisFrequency);
Sampled_shortTermAnalysis (me, windowDuration, dt, & nFrames, & t1);
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
// find out the size of the FFT
long nfft = 2;
while (nfft < sframe -> nx) nfft *= 2;
long nq = nfft / 2 + 1;
double qmax = 0.5 * nfft / samplingFrequency, dq = qmax / (nq - 1);
autoPowerCepstrogram thee = PowerCepstrogram_create (my xmin, my xmax, nFrames, dt, t1, 0, qmax, nq, dq, 0);
autoMelderProgress progress (L"Cepstrogram analysis");
for (long iframe = 1; iframe <= nFrames; iframe++) {
double t = Sampled_indexToX (thee.peek(), iframe);
Sound_into_Sound (sound.peek(), sframe.peek(), t - windowDuration / 2);
Vector_subtractMean (sframe.peek());
Sounds_multiply (sframe.peek(), window.peek());
autoSpectrum spec = Sound_to_Spectrum (sframe.peek(), 1); // FFT yes
autoPowerCepstrum cepstrum = Spectrum_to_PowerCepstrum (spec.peek());
for (long i = 1; i <= nq; i++) {
thy z[i][iframe] = cepstrum -> z[1][i];
}
if ((iframe % 10) == 1) {
Melder_progress ((double) iframe / nFrames, L"PowerCepstrogram analysis of frame ",
Melder_integer (iframe), L" out of ", Melder_integer (nFrames), L".");
}
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, ": no PowerCepstrogram created.");
}
}
Cepstrogram Sound_to_Cepstrogram (Sound me, double analysisWidth, double dt, double maximumFrequency) {
try {
double windowDuration = 2 * analysisWidth; /* gaussian window */
long nFrames;
// Convenience: analyse the whole sound into one Cepstrogram_frame
if (windowDuration > my dx * my nx) {
windowDuration = my dx * my nx;
}
double t1, samplingFrequency = 2 * maximumFrequency;
autoSound sound = Sound_resample (me, samplingFrequency, 50);
Sampled_shortTermAnalysis (me, windowDuration, dt, & nFrames, & t1);
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
double qmin, qmax, dq, q1;
long nq;
{ // laziness: find out the proper dimensions
autoSpectrum spec = Sound_to_Spectrum (sframe.peek(), 1);
autoCepstrum cepstrum = Spectrum_to_Cepstrum (spec.peek());
qmin = cepstrum -> xmin; qmax = cepstrum -> xmax; dq = cepstrum -> dx;
q1 = cepstrum -> x1; nq = cepstrum -> nx;
}
autoCepstrogram thee = Cepstrogram_create (my xmin, my xmax, nFrames, dt, t1, qmin, qmax, nq, dq, q1);
autoMelderProgress progress (L"Cepstrogram analysis");
for (long iframe = 1; iframe <= nFrames; iframe++) {
double t = Sampled_indexToX (thee.peek(), iframe);
Sound_into_Sound (sound.peek(), sframe.peek(), t - windowDuration / 2);
Vector_subtractMean (sframe.peek());
Sounds_multiply (sframe.peek(), window.peek());
autoSpectrum spec = Sound_to_Spectrum (sframe.peek(), 1);
autoCepstrum cepstrum = Spectrum_to_Cepstrum (spec.peek());
for (long i = 1; i <= nq; i++) {
thy z[i][iframe] = cepstrum -> z[1][i];
}
if ((iframe % 10) == 1) {
Melder_progress ((double) iframe / nFrames, L"Cepstrogram analysis of frame ",
Melder_integer (iframe), L" out of ", Melder_integer (nFrames), L".");
}
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, ": no Cepstrogram created.");
}
}
static autoIntensity Sound_to_Intensity_ (Sound me, double minimumPitch, double timeStep, int subtractMeanPressure) {
try {
/*
* Preconditions.
*/
if (! NUMdefined (minimumPitch)) Melder_throw (U"(Sound-to-Intensity:) Minimum pitch undefined.");
if (! NUMdefined (timeStep)) Melder_throw (U"(Sound-to-Intensity:) Time step undefined.");
if (timeStep < 0.0) Melder_throw (U"(Sound-to-Intensity:) Time step should be zero or positive instead of ", timeStep, U".");
if (my dx <= 0.0) Melder_throw (U"(Sound-to-Intensity:) The Sound's time step should be positive.");
if (minimumPitch <= 0.0) Melder_throw (U"(Sound-to-Intensity:) Minimum pitch should be positive.");
/*
* Defaults.
*/
if (timeStep == 0.0) timeStep = 0.8 / minimumPitch; // default: four times oversampling Hanning-wise
double windowDuration = 6.4 / minimumPitch;
Melder_assert (windowDuration > 0.0);
double halfWindowDuration = 0.5 * windowDuration;
long halfWindowSamples = (long) floor (halfWindowDuration / my dx);
autoNUMvector <double> amplitude (- halfWindowSamples, halfWindowSamples);
autoNUMvector <double> window (- halfWindowSamples, halfWindowSamples);
for (long i = - halfWindowSamples; i <= halfWindowSamples; i ++) {
double x = i * my dx / halfWindowDuration, root = 1 - x * x;
window [i] = root <= 0.0 ? 0.0 : NUMbessel_i0_f ((2 * NUMpi * NUMpi + 0.5) * sqrt (root));
}
long numberOfFrames;
double thyFirstTime;
try {
Sampled_shortTermAnalysis (me, windowDuration, timeStep, & numberOfFrames, & thyFirstTime);
} catch (MelderError) {
Melder_throw (U"The duration of the sound in an intensity analysis should be at least 6.4 divided by the minimum pitch (", minimumPitch, U" Hz), "
U"i.e. at least ", 6.4 / minimumPitch, U" s, instead of ", my xmax - my xmin, U" s.");
}
autoIntensity thee = Intensity_create (my xmin, my xmax, numberOfFrames, timeStep, thyFirstTime);
for (long iframe = 1; iframe <= numberOfFrames; iframe ++) {
double midTime = Sampled_indexToX (thee.peek(), iframe);
long midSample = Sampled_xToNearestIndex (me, midTime);
long leftSample = midSample - halfWindowSamples, rightSample = midSample + halfWindowSamples;
double sumxw = 0.0, sumw = 0.0, intensity;
if (leftSample < 1) leftSample = 1;
if (rightSample > my nx) rightSample = my nx;
for (long channel = 1; channel <= my ny; channel ++) {
for (long i = leftSample; i <= rightSample; i ++) {
amplitude [i - midSample] = my z [channel] [i];
}
if (subtractMeanPressure) {
double sum = 0.0;
for (long i = leftSample; i <= rightSample; i ++) {
sum += amplitude [i - midSample];
}
double mean = sum / (rightSample - leftSample + 1);
for (long i = leftSample; i <= rightSample; i ++) {
amplitude [i - midSample] -= mean;
}
}
for (long i = leftSample; i <= rightSample; i ++) {
sumxw += amplitude [i - midSample] * amplitude [i - midSample] * window [i - midSample];
sumw += window [i - midSample];
}
}
intensity = sumxw / sumw;
intensity /= 4e-10;
thy z [1] [iframe] = intensity < 1e-30 ? -300 : 10 * log10 (intensity);
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": intensity analysis not performed.");
}
}
FormantFilter Sound_and_Pitch_to_FormantFilter (Sound me, Pitch thee, double analysisWidth, double dt,
double f1_hz, double fmax_hz, double df_hz, double relative_bw) {
try {
double t1, windowDuration = 2 * analysisWidth; /* gaussian window */
double nyquist = 0.5 / my dx, samplingFrequency = 2 * nyquist, fmin_hz = 0;
long nt, f0_undefined = 0;
if (my xmin > thy xmin || my xmax > thy xmax) Melder_throw
("The domain of the Sound is not included in the domain of the Pitch.");
double f0_median = Pitch_getQuantile (thee, thy xmin, thy xmax, 0.5, kPitch_unit_HERTZ);
if (f0_median == NUMundefined || f0_median == 0) {
f0_median = 100;
Melder_warning (L"Pitch values undefined. Bandwith fixed to 100 Hz. ");
}
if (f1_hz <= 0) {
f1_hz = 100;
}
if (fmax_hz <= 0) {
fmax_hz = nyquist;
}
if (df_hz <= 0) {
df_hz = f0_median / 2;
}
if (relative_bw <= 0) {
relative_bw = 1.1;
}
fmax_hz = MIN (fmax_hz, nyquist);
long nf = floor ( (fmax_hz - f1_hz) / df_hz + 0.5);
Sampled_shortTermAnalysis (me, windowDuration, dt, &nt, &t1);
autoFormantFilter him = FormantFilter_create (my xmin, my xmax, nt, dt, t1,
fmin_hz, fmax_hz, nf, df_hz, f1_hz);
// Temporary objects
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoMelderProgress progress (L"Sound & Pitch: To FormantFilter");
for (long i = 1; i <= nt; i++) {
double t = Sampled_indexToX (him.peek(), i);
double b, f0 = Pitch_getValueAtTime (thee, t, kPitch_unit_HERTZ, 0);
if (f0 == NUMundefined || f0 == 0) {
f0_undefined++; f0 = f0_median;
}
b = relative_bw * f0;
Sound_into_Sound (me, sframe.peek(), t - windowDuration / 2);
Sounds_multiply (sframe.peek(), window.peek());
Sound_into_FormantFilter_frame (sframe.peek(), him.peek(), i, b);
if ( (i % 10) == 1) {
Melder_progress ( (double) i / nt, L"Frame ", Melder_integer (i), L" out of ",
Melder_integer (nt), L".");
}
}
double ref = FilterBank_DBREF * gaussian_window_squared_correction (window -> nx);
NUMdmatrix_to_dBs (his z, 1, his ny, 1, his nx, ref, FilterBank_DBFAC, FilterBank_DBFLOOR);
return him.transfer();
} catch (MelderError) {
Melder_throw ("FormantFilter not created from Pitch & FormantFilter.");
}
}
MelFilter Sound_to_MelFilter (Sound me, double analysisWidth, double dt,
double f1_mel, double fmax_mel, double df_mel) {
try {
double t1, samplingFrequency = 1 / my dx, nyquist = 0.5 * samplingFrequency;
double windowDuration = 2 * analysisWidth; /* gaussian window */
double fmin_mel = 0;
double fbottom = HZTOMEL (100.0), fceiling = HZTOMEL (nyquist);
long nt, frameErrorCount = 0;
// Check defaults.
if (fmax_mel <= 0 || fmax_mel > fceiling) {
fmax_mel = fceiling;
}
if (fmax_mel <= f1_mel) {
f1_mel = fbottom; fmax_mel = fceiling;
}
if (f1_mel <= 0) {
f1_mel = fbottom;
}
if (df_mel <= 0) {
df_mel = 100.0;
}
// Determine the number of filters.
long nf = floor ( (fmax_mel - f1_mel) / df_mel + 0.5);
fmax_mel = f1_mel + nf * df_mel;
Sampled_shortTermAnalysis (me, windowDuration, dt, &nt, &t1);
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoMelFilter thee = MelFilter_create (my xmin, my xmax, nt, dt, t1, fmin_mel,
fmax_mel, nf, df_mel, f1_mel);
autoMelderProgress progress (L"MelFilters analysis");
for (long i = 1; i <= nt; i++) {
double t = Sampled_indexToX (thee.peek(), i);
Sound_into_Sound (me, sframe.peek(), t - windowDuration / 2);
Sounds_multiply (sframe.peek(), window.peek());
if (! Sound_into_MelFilter_frame (sframe.peek(), thee.peek(), i)) {
frameErrorCount++;
}
if ( (i % 10) == 1) {
Melder_progress ( (double) i / nt, L"Frame ", Melder_integer (i), L" out of ",
Melder_integer (nt), L".");
}
}
if (frameErrorCount) Melder_warning (L"Analysis results of ", Melder_integer (frameErrorCount),
L" frame(s) out of ", Melder_integer (nt), L" will be suspect.");
// Window correction.
double ref = FilterBank_DBREF * gaussian_window_squared_correction (window -> nx);
NUMdmatrix_to_dBs (thy z, 1, thy ny, 1, thy nx, ref, FilterBank_DBFAC, FilterBank_DBFLOOR);
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, ": no MelFilter created.");
}
}
Pitch Sound_to_Pitch_any (Sound me, double dt, /*timeStepStradygy related*/
double minimumPitch, /*Pitch settings realted*/
double periodsPerWindow, /*kTimeSoundAnalysisEditor_pitch_analysisMethod related*/
int maxnCandidates,
int method, /*method related*/
double silenceThreshold, double voicingThreshold, double octaveCost, double octaveJumpCost,
double voicedUnvoicedCost, double ceiling)
{
NUMfft_Table fftTable = NUMfft_Table_create();
double duration, t1;
double dt_window; /* Window length in seconds. */
long nsamp_window, halfnsamp_window; /* Number of samples per window. */
long nFrames, minimumLag, maximumLag;
long iframe, nsampFFT;
double interpolation_depth;
long nsamp_period, halfnsamp_period; /* Number of samples in longest period. */
long brent_ixmax, brent_depth;
double brent_accuracy; /* Obsolete. */
double globalPeak;
if (maxnCandidates < 2 || method < AC_HANNING && method > FCC_ACCURATE)
{
std::cout<<"Error: maxnCandidates: "<<maxnCandidates<<" method: "<<method<<"."<<std::endl;
std::cout<<"Sound_to_Pitch.cpp: Line 13. 69"<<std::endl;
return NULL;
}
if (maxnCandidates < ceiling / minimumPitch) maxnCandidates = ceiling / minimumPitch;
if (dt <= 0.0) dt = periodsPerWindow / minimumPitch / 4.0; /* e.g. 3 periods, 75 Hz: 10 milliseconds. */
switch (method) {
case AC_HANNING:
brent_depth = NUM_PEAK_INTERPOLATE_SINC70;
brent_accuracy = 1e-7;
interpolation_depth = 0.5;
break;
case AC_GAUSS:
periodsPerWindow *= 2; /* Because Gaussian window is twice as long. */
brent_depth = NUM_PEAK_INTERPOLATE_SINC700;
brent_accuracy = 1e-11;
interpolation_depth = 0.25; /* Because Gaussian window is twice as long. */
break;
case FCC_NORMAL:
brent_depth = NUM_PEAK_INTERPOLATE_SINC70;
brent_accuracy = 1e-7;
interpolation_depth = 1.0;
break;
case FCC_ACCURATE:
brent_depth = NUM_PEAK_INTERPOLATE_SINC700;
brent_accuracy = 1e-11;
interpolation_depth = 1.0;
break;
}
duration = my dx * my nx;
if (minimumPitch < periodsPerWindow / duration) {
std::cout<<"To analyse this Sound, minimum pitch must not be less than "<< periodsPerWindow / duration<<" Hz."<<std::endl;
std::cout<<"Sound_to_Pitch.cpp: Line 31.103"<<std::endl;
return NULL;
}
/*
* Determine the number of samples in the longest period.
* We need this to compute the local mean of the sound (looking one period in both directions),
* and to compute the local peak of the sound (looking half a period in both directions).
*/
nsamp_period = floor(1 / my dx / minimumPitch);
halfnsamp_period = nsamp_period / 2 + 1;
if (ceiling > 0.5 / my dx) ceiling = 0.5 / my dx;
// Determine window length in seconds and in samples.
dt_window = periodsPerWindow / minimumPitch;
nsamp_window = floor (dt_window / my dx);
halfnsamp_window = nsamp_window / 2 - 1;
if (halfnsamp_window < 2){
std::cout<<"Analysis window too short."<<std::endl;
std::cout<<"Sound_to_Pitch.cpp: Line 31.123"<<std::endl;
return NULL;
}
nsamp_window = halfnsamp_window * 2;
// Determine the minimum and maximum lags.
minimumLag = floor (1 / my dx / ceiling);
if (minimumLag < 2) minimumLag = 2;
maximumLag = floor (nsamp_window / periodsPerWindow) + 2;
if (maximumLag > nsamp_window) maximumLag = nsamp_window;
/*
* Determine the number of frames.
* Fit as many frames as possible symmetrically in the total duration.
* We do this even for the forward cross-correlation method,
* because that allows us to compare the two methods.
*/
if(!Sampled_shortTermAnalysis (me, method >= FCC_NORMAL ? 1 / minimumPitch + dt_window : dt_window, dt, & nFrames, & t1)){
std::cout<<"The pitch analysis would give zero pitch frames."<<std::endl;
std::cout<<"Sound_to_Pitch.cpp: Line 31.142"<<std::endl;
return NULL;
}
// Create the resulting pitch contour.
Pitch thee = Pitch_create (my xmin, my xmax, nFrames, dt, t1, ceiling, maxnCandidates);
// Compute the global absolute peak for determination of silence threshold.
globalPeak = 0.0;
for (long channel = 1; channel <= my ny; channel ++) {
double mean = 0.0;
for (long i = 1; i <= my nx; i ++) {
mean += my z [channel] [i];
}
mean /= my nx;
for (long i = 1; i <= my nx; i ++) {
double value = fabs (my z [channel] [i] - mean);
if (value > globalPeak) globalPeak = value;
}
}
if (globalPeak == 0.0) return thee;
double **frame, *ac, *window, *windowR;
if (method >= FCC_NORMAL) { /* For cross-correlation analysis. */
// Create buffer for cross-correlation analysis.
frame = (double **)malloc(sizeof(double *) * (my ny + 1));
for(long i = 1; i <= my ny; ++ i){
frame[i] = (double *)malloc(sizeof(double) * (nsamp_window + 1));
for(long j = 1; j <= nsamp_window; ++ j)
frame[i][j] = 0.0;
} /****frame.reset (1, my ny, 1, nsamp_window);****/
brent_ixmax = nsamp_window * interpolation_depth;
} else { /* For autocorrelation analysis. */
/*
* Compute the number of samples needed for doing FFT.
* To avoid edge effects, we have to append zeroes to the window.
* The maximum lag considered for maxima is maximumLag.
* The maximum lag used in interpolation is nsamp_window * interpolation_depth.
*/
nsampFFT = 1;
while (nsampFFT < nsamp_window * (1 + interpolation_depth)) nsampFFT *= 2;
// Create buffers for autocorrelation analysis.
frame = (double **)malloc(sizeof(double *) * (my ny + 1));
for(long i = 1; i <= my ny; ++ i){
frame [i] = (double *)malloc(sizeof(double) * (nsampFFT + 1));
for(long j = 0; j <= nsampFFT; ++ j)
frame[i][j] = 0.0;
} /****frame.reset (1, my ny, 1, nsampFFT);****/
window = (double *)malloc(sizeof(double) * (nsamp_window + 1));
for(long i = 0; i <= nsamp_window; ++ i)
window[i] = 0.0;
/****window.reset (1, nsamp_window);****/
windowR = (double *)malloc(sizeof(double) * (nsampFFT + 1));
ac = (double *)malloc(sizeof(double) * (nsampFFT + 1));
for(long i = 0; i <= nsampFFT; ++ i)
windowR[i] = ac[i] = 0.0;
/****windowR.reset (1, nsampFFT); ac.reset (1, nsampFFT); ****/
NUMfft_Table_init (fftTable, nsampFFT);
/*
* A Gaussian or Hanning window is applied against phase effects.
* The Hanning window is 2 to 5 dB better for 3 periods/window.
* The Gaussian window is 25 to 29 dB better for 6 periods/window.
*/
if (method == AC_GAUSS) { /* Gaussian window. */
double imid = 0.5 * (nsamp_window + 1), edge = exp (-12.0);
for (long i = 1; i <= nsamp_window; i ++)
window[i] = (exp(-48.0*(i-imid)*(i-imid) /
(nsamp_window + 1) / (nsamp_window + 1)) - edge) / (1 - edge);
} else { /* Hanning window*/
for (long i = 1; i <= nsamp_window; i ++)
window [i] = 0.5 - 0.5 * cos (i * 2 * NUMpi / (nsamp_window + 1));
}
// Compute the normalized autocorrelation of the window.
for (long i = 1; i <= nsamp_window; i ++) windowR [i] = window [i];
NUMfft_forward (fftTable, windowR);
windowR [1] *= windowR [1]; // DC component
for (long i = 2; i < nsampFFT; i += 2) {
windowR [i] = windowR [i] * windowR [i] + windowR [i+1] * windowR [i+1];
windowR [i + 1] = 0.0; // power spectrum: square and zero
}
windowR [nsampFFT] *= windowR [nsampFFT]; // Nyquist frequency
NUMfft_backward (fftTable, windowR); // autocorrelation
for (long i = 2; i <= nsamp_window; i ++) windowR [i] /= windowR [1]; // normalize
windowR [1] = 1.0; // normalize
brent_ixmax = nsamp_window * interpolation_depth;
}
double *r = (double *) malloc( sizeof(double) * (2 * (nsamp_window + 1) + 1) );
r += nsamp_window + 1; //make "r" become a symetrical vectr
long *imax = (long *) malloc( sizeof(long) * (maxnCandidates + 1));
double *localMean = (double *) malloc( sizeof(double) * (my ny + 1));
for(iframe = 1; iframe <= nFrames; iframe ++){
Pitch_Frame pitchFrame = & thy frame[iframe];
double t = thy x1 + (iframe - 1) *(thy dx), localPeak;
long leftSample = (long) floor((t - my x1) / my dx) + 1;
long rightSample = leftSample + 1;
long startSample, endSample;
for(long channel = 1; channel <= my ny; ++ channel){ //Compute the local mean; look one longest period to both sides.
startSample = rightSample - nsamp_period;
endSample = leftSample + nsamp_period;
if ( startSample < 0 ) {
std::cout<<"StartSample < 1"<<std::endl;
std::cout<<"Sound_to_Pitch.cpp: Line 31"<<std::endl;
return NULL;
}
if (endSample > my nx){
std::cout<<"EndSample > my nx"<<std::endl;
std::cout<<"Sound_to_Pitch.cpp: Line 31.262"<<std::endl;
return NULL;
}
localMean[channel] = 0.0;
for (long i = startSample; i <= endSample; i ++) {
localMean[channel] += my z[channel][i];
}
localMean[channel] /= 2 * nsamp_period;
// Copy a window to a frame and subtract the local mean. We are going to kill the DC component before windowing.
startSample = rightSample - halfnsamp_window;
endSample = leftSample + halfnsamp_window;
if ( startSample < 1 ) {
std::cout<<"StartSample < 1"<<std::endl;
std::cout<<"Sound_to_Pitch.cpp: Line 31.281"<<std::endl;
return NULL;
}
if (endSample > my nx){
std::cout<<"EndSample > my nx"<<std::endl;
std::cout<<"Sound_to_Pitch.cpp: Line 31.287"<<std::endl;
return NULL;
}
if (method < FCC_NORMAL) {
for (long j = 1, i = startSample; j <= nsamp_window; j ++)
frame [channel] [j] = (my z [channel] [i ++] - localMean [channel]) * window [j];
for (long j = nsamp_window + 1; j <= nsampFFT; j ++)
frame [channel] [j] = 0.0;
} else {
for (long j = 1, i = startSample; j <= nsamp_window; j ++)
frame [channel] [j] = my z [channel] [i ++] - localMean [channel];
}
}
// Compute the local peak; look half a longest period to both sides.
localPeak = 0.0;
if ((startSample = halfnsamp_window + 1 - halfnsamp_period) < 1) startSample = 1;
if ((endSample = halfnsamp_window + halfnsamp_period) > nsamp_window) endSample = nsamp_window;
for (long channel = 1; channel <= my ny; channel ++) {
for (long j = startSample; j <= endSample; j ++) {
double value = fabs (frame [channel] [j]);
if (value > localPeak) localPeak = value;
}
}
pitchFrame->intensity = localPeak > globalPeak ? 1.0 : localPeak / globalPeak;
// Compute the correlation into the array 'r'.
if (method >= FCC_NORMAL) {
double startTime = t - 0.5 * (1.0 / minimumPitch + dt_window);
long localSpan = maximumLag + nsamp_window, localMaximumLag, offset;
if ((startSample = (long) floor ((startTime - my x1) / my dx)) + 1 < 1)
startSample = 1;
if (localSpan > my nx + 1 - startSample) localSpan = my nx + 1 - startSample;
localMaximumLag = localSpan - nsamp_window;
offset = startSample - 1;
double sumx2 = 0; /* Sum of squares. */
for (long channel = 1; channel <= my ny; channel ++) { ///channel = 1; channel <= my ny
double *amp = my z[channel] + offset;
for (long i = 1; i <= nsamp_window; i ++) { ///i = 1; i <= nsamp_window
double x = amp[i] - localMean[channel];
sumx2 += x * x;
}
}
double sumy2 = sumx2; /* At zero lag, these are still equal. */
r[0] = 1.0;
for (long i = 1; i <= localMaximumLag; i ++) {
double product = 0.0;
for (long channel = 1; channel <= my ny; channel ++) { ///channel = 1; channel <= my ny
double *amp = my z[channel] + offset;
double y0 = amp[i] - localMean[channel];
double yZ = amp[i + nsamp_window] - localMean[channel];
sumy2 += yZ * yZ - y0 * y0;
for (long j = 1; j <= nsamp_window; j ++) { ///j = 1; j <= nsamp_window
double x = amp[j] - localMean[channel];
double y = amp[i + j] - localMean[channel];
product += x * y;
}
}
r[- i] = r[i] = product / sqrt (sumx2 * sumy2);
}
} else {
// The FFT of the autocorrelation is the power spectrum.
for (long i = 1; i <= nsampFFT; i ++)
ac [i] = 0.0;
for (long channel = 1; channel <= my ny; channel ++) {
NUMfft_forward (fftTable, frame [channel]); /* Complex spectrum. */
ac [1] += frame [channel] [1] * frame [channel] [1]; /* DC component. */
for (long i = 2; i < nsampFFT; i += 2) {
ac [i] += frame [channel] [i] * frame [channel] [i] + frame [channel] [i+1] * frame [channel] [i+1]; /* Power spectrum. */
}
ac [nsampFFT] += frame [channel] [nsampFFT] * frame [channel] [nsampFFT]; /* Nyquist frequency. */
}
NUMfft_backward (fftTable, ac); /* Autocorrelation. */
/*
* Normalize the autocorrelation to the value with zero lag,
* and divide it by the normalized autocorrelation of the window.
*/
r [0] = 1.0;
for (long i = 1; i <= brent_ixmax; i ++)
r [- i] = r [i] = ac [i + 1] / (ac [1] * windowR [i + 1]);
}
// Create (too much) space for candidates
Pitch_Frame_init (pitchFrame, maxnCandidates);
// Register the first candidate, which is always present: voicelessness.
pitchFrame->nCandidates = 1;
pitchFrame->candidate[1].frequency = 0.0; /* Voiceless: always present. */
pitchFrame->candidate[1].strength = 0.0;
/*
* Shortcut: absolute silence is always voiceless.
* Go to next frame.
*/
if (localPeak == 0) continue;
/*
* Find the strongest maxima of the correlation of this frame,
* and register them as candidates.
*/
imax[1] = 0;
for (long i = 2; i < maximumLag && i < brent_ixmax; i ++)
if (r[i] > 0.5 * voicingThreshold && /* Not too unvoiced? */
r[i] > r[i-1] && r[i] >= r[i+1]) /* Maximum ? */
{
int place = 0;
// Use parabolic interpolation for first estimate of frequency,and sin(x)/x interpolation to compute the strength of this frequency.
double dr = 0.5 * (r[i+1] - r[i-1]);
double d2r = 2 * r[i] - r[i-1] - r[i+1];
double frequencyOfMaximum = 1 / my dx / (i + dr / d2r);
long offset = - brent_ixmax - 1;
double strengthOfMaximum = /* method & 1 ? */
NUM_interpolate_sinc (& r[offset], brent_ixmax - offset, 1 / my dx / frequencyOfMaximum - offset, 30)
/* : r [i] + 0.5 * dr * dr / d2r */;
/* High values due to short windows are to be reflected around 1. */
if (strengthOfMaximum > 1.0) strengthOfMaximum = 1.0 / strengthOfMaximum;
// Find a place for this maximum.
if (pitchFrame->nCandidates < thy maxnCandidates) { /* Is there still a free place? */
place = ++ pitchFrame->nCandidates;
} else {
/* Try the place of the weakest candidate so far. */
double weakest = 2;
for (int iweak = 2; iweak <= thy maxnCandidates; iweak ++) { //iweak = 2; iweak <= thy maxnCandidates;
/* High frequencies are to be favoured */
/* if we want to analyze a perfectly periodic signal correctly. */
double localStrength = pitchFrame->candidate[iweak].strength - octaveCost *
NUMlog2 (minimumPitch / pitchFrame->candidate[iweak].frequency);
if (localStrength < weakest) {
weakest = localStrength;
place = iweak;
}
}
/* If this maximum is weaker than the weakest candidate so far, give it no place. */
if (strengthOfMaximum - octaveCost * NUMlog2 (minimumPitch / frequencyOfMaximum) <= weakest)
place = 0;
}
if (place) { /* Have we found a place for this candidate? */
pitchFrame->candidate[place].frequency = frequencyOfMaximum;
pitchFrame->candidate[place].strength = strengthOfMaximum;
imax [place] = i;
}
}
// Second pass: for extra precision, maximize sin(x)/x interpolation ('sinc').
for (long i = 2; i <= pitchFrame->nCandidates; i ++) {
if (method != AC_HANNING || pitchFrame->candidate[i].frequency > 0.0 / my dx) {
double xmid, ymid;
long offset = - brent_ixmax - 1;
ymid = NUMimproveMaximum (& r[offset], brent_ixmax - offset, imax[i] - offset,
pitchFrame->candidate[i].frequency > 0.3 / my dx ? NUM_PEAK_INTERPOLATE_SINC700 : brent_depth, & xmid);
xmid += offset;
pitchFrame->candidate[i].frequency = 1.0 / my dx / xmid;
if (ymid > 1.0) ymid = 1.0 / ymid;
pitchFrame->candidate[i].strength = ymid;
}
}
} /* Next frame. */
Pitch_pathFinder (thee, silenceThreshold, voicingThreshold,octaveCost, octaveJumpCost,
voicedUnvoicedCost, ceiling, false);
//false: Melder_debug == 31 ? true : false Melder_debug 31: Pitch analysis: formant pulling on
return thee;
}
SPINET Sound_to_SPINET (Sound me, double timeStep, double windowDuration,
double minimumFrequencyHz, double maximumFrequencyHz, long nFilters,
double excitationErbProportion, double inhibitionErbProportion)
{
Sound window = NULL, frame = NULL; SPINET thee = NULL;
long i, j, k, numberOfFrames;
double firstTime, b = 1.02, samplingFrequency = 1 / my dx;
double *f = NULL, *bw = NULL, *aex = NULL, *ain = NULL;
if (timeStep < my dx) timeStep = my dx;
if (maximumFrequencyHz > samplingFrequency / 2) maximumFrequencyHz = samplingFrequency / 2;
if (! Sampled_shortTermAnalysis (me, windowDuration, timeStep, &numberOfFrames, &firstTime) ||
! (thee = SPINET_create (my xmin, my xmax, numberOfFrames, timeStep, firstTime,
minimumFrequencyHz, maximumFrequencyHz, nFilters,
excitationErbProportion, inhibitionErbProportion)) ||
! (window = Sound_createGaussian (windowDuration, samplingFrequency)) ||
! (frame = Sound_createSimple (1, windowDuration, samplingFrequency)) ||
! (f = NUMdvector (1, nFilters)) || ! (bw = NUMdvector (1, nFilters)) ||
! (aex = NUMdvector (1, nFilters)) || ! (ain = NUMdvector (1, nFilters))) goto cleanup;
/*
Cochlear filterbank: gammatone
*/
for (i=1; i <= nFilters; i++)
{
f[i] = NUMerbToHertz (thy y1 + (i - 1) * thy dy);
bw[i] = 2 * NUMpi * b * (f[i] * (6.23e-6 * f[i] + 93.39e-3) + 28.52);
}
Melder_progress1 (0.0, L"SPINET analysis");
for (i=1; i <= nFilters; i++)
{
Sound gammaTone = NULL, filtered = NULL;
/* Contribution of outer & middle ear and phase locking */
double bb = (f[i] / 1000) * exp (- f[i] / 1000);
/* Time where gammafunction envelope has its maximum */
double tgammaMax = (thy gamma - 1) / bw[i];
/* Amplitude at tgammaMax */
double gammaMaxAmplitude = pow ((thy gamma - 1) / (NUMe * bw[i]), (thy gamma - 1));
double timeCorrection = tgammaMax - windowDuration / 2;
if (! (gammaTone = Sound_createGammaTone (0, 0.1, samplingFrequency,
thy gamma, b, f[i], 0, 0, 0)) ||
/* filtering can be made 30% faster by taking Spectrum(me) outside the loop */
! (filtered = Sounds_convolve (me, gammaTone, kSounds_convolve_scaling_SUM, kSounds_convolve_signalOutsideTimeDomain_ZERO))) { forget (gammaTone); goto cleanup; }
/*
To energy measure: weigh with broad-band transfer function
*/
for (j=1; j <= numberOfFrames; j++)
{
Sound_into_Sound (filtered, frame, Sampled_indexToX (thee, j) + timeCorrection);
Sounds_multiply (frame, window);
thy y[i][j] = Sound_power (frame) * bb / gammaMaxAmplitude;
}
forget (filtered); forget (gammaTone);
if (! Melder_progress5 ((double)i / nFilters, L"SPINET: filter ", Melder_integer (i), L" from ",
Melder_integer (nFilters), L".")) goto cleanup;
}
/*
Excitatory and inhibitory area functions
*/
for (i=1; i <= nFilters; i++)
{
for (k=1; k <= nFilters; k++)
{
double fr = (f[k] - f[i]) / bw[i];
aex[i] += fgamma (fr / thy excitationErbProportion, thy gamma);
ain[i] += fgamma (fr / thy inhibitionErbProportion, thy gamma);
}
}
/*
On-center off-surround interactions
*/
for (j=1; j <= numberOfFrames; j++)
for (i=1; i <= nFilters; i++)
{
double a = 0;
for (k=1; k <= nFilters; k++)
{
double fr = (f[k] - f[i]) / bw[i];
double hexsq = fgamma (fr / thy excitationErbProportion, thy gamma);
double hinsq = fgamma (fr / thy inhibitionErbProportion, thy gamma);
a += thy y[k][j] * (hexsq / aex[i] - hinsq / ain[i]);
}
thy s[i][j] = a > 0 ? a : 0;
}
Melder_progress1 (1.0, NULL);
cleanup:
NUMdvector_free (aex, 1); NUMdvector_free (ain, 1);
NUMdvector_free (f, 1); NUMdvector_free (bw, 1);
forget (window); forget (frame);
if (! Melder_hasError()) return thee;
forget (thee);
return Melder_errorp1 (L"Sound_to_SPINET: not performed.");
}
LPC LPC_and_Sound_to_LPC_robust (LPC thee, Sound me, double analysisWidth, double preEmphasisFrequency, double k,
int itermax, double tol, int wantlocation) {
struct huber_struct struct_huber = { 0 };
try {
double t1, samplingFrequency = 1.0 / my dx, tol_svd = 0.000001;
double location = 0, windowDuration = 2 * analysisWidth; /* Gaussian window */
long nFrames, frameErrorCount = 0, iter = 0;
long p = thy maxnCoefficients;
if (my xmin != thy xmin || my xmax != thy xmax) {
Melder_throw ("Time domains differ.");
}
if (my dx != thy samplingPeriod) {
Melder_throw ("Sampling intervals differ.");
}
if (floor (windowDuration / my dx) < p + 1) {
Melder_throw ("Analysis window too short.");
}
Sampled_shortTermAnalysis (me, windowDuration, thy dx, & nFrames, & t1);
if (nFrames != thy nx || t1 != thy x1) {
Melder_throw ("Incorrect retrieved analysis width");
}
autoSound sound = Data_copy (me);
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoLPC him = Data_copy (thee);
huber_struct_init (&struct_huber, windowDuration, p, samplingFrequency, location, wantlocation);
struct_huber.k = k;
struct_huber.tol = tol;
struct_huber.tol_svd = tol_svd;
struct_huber.itermax = itermax;
autoMelderProgress progess (L"LPC analysis");
Sound_preEmphasis (sound.peek(), preEmphasisFrequency);
for (long i = 1; i <= nFrames; i++) {
LPC_Frame lpc = (LPC_Frame) & thy d_frames[i];
LPC_Frame lpcto = (LPC_Frame) & his d_frames[i];
double t = Sampled_indexToX (thee, i);
Sound_into_Sound (sound.peek(), sframe.peek(), t - windowDuration / 2);
Vector_subtractMean (sframe.peek());
Sounds_multiply (sframe.peek(), window.peek());
try {
LPC_Frames_and_Sound_huber (lpc, sframe.peek(), lpcto, & struct_huber);
} catch (MelderError) {
frameErrorCount++;
}
iter += struct_huber.iter;
if ( (i % 10) == 1) {
Melder_progress ( (double) i / nFrames, L"LPC analysis of frame ",
Melder_integer (i), L" out of ", Melder_integer (nFrames), L".");
}
}
if (frameErrorCount) Melder_warning (L"Results of ", Melder_integer (frameErrorCount),
L" frame(s) out of ", Melder_integer (nFrames), L" could not be optimised.");
MelderInfo_writeLine4 (L"Number of iterations: ", Melder_integer (iter),
L"\n Average per frame: ", Melder_double (((double) iter) / nFrames));
huber_struct_destroy (&struct_huber);
return him.transfer();
} catch (MelderError) {
huber_struct_destroy (&struct_huber);
Melder_throw (me, ": no robust LPC created.");
}
}
Pitch Sound_to_Pitch_shs (Sound me, double timeStep, double minimumPitch,
double maximumFrequency, double ceiling, long maxnSubharmonics, long maxnCandidates,
double compressionFactor, long nPointsPerOctave) {
try {
double firstTime, newSamplingFrequency = 2 * maximumFrequency;
double windowDuration = 2 / minimumPitch, halfWindow = windowDuration / 2;
double atans = nPointsPerOctave * NUMlog2 (65.0 / 50.0) - 1;
// Number of speech samples in the downsampled signal in each frame:
// 100 for windowDuration == 0.04 and newSamplingFrequency == 2500
long nx = lround (windowDuration * newSamplingFrequency);
// The minimum number of points for the fft is 256.
long nfft = 1;
while ( (nfft *= 2) < nx || nfft <= 128) {
;
}
long nfft2 = nfft / 2 + 1;
double frameDuration = nfft / newSamplingFrequency;
double df = newSamplingFrequency / nfft;
// The number of points on the octave scale
double fminl2 = NUMlog2 (minimumPitch), fmaxl2 = NUMlog2 (maximumFrequency);
long nFrequencyPoints = (long) floor ((fmaxl2 - fminl2) * nPointsPerOctave);
double dfl2 = (fmaxl2 - fminl2) / (nFrequencyPoints - 1);
autoSound sound = Sound_resample (me, newSamplingFrequency, 50);
long numberOfFrames;
Sampled_shortTermAnalysis (sound.peek(), windowDuration, timeStep, &numberOfFrames, &firstTime);
autoSound frame = Sound_createSimple (1, frameDuration, newSamplingFrequency);
autoSound hamming = Sound_createHamming (nx / newSamplingFrequency, newSamplingFrequency);
autoPitch thee = Pitch_create (my xmin, my xmax, numberOfFrames, timeStep, firstTime,
ceiling, maxnCandidates);
autoNUMvector<double> cc (1, numberOfFrames);
autoNUMvector<double> specAmp (1, nfft2);
autoNUMvector<double> fl2 (1, nfft2);
autoNUMvector<double> yv2 (1, nfft2);
autoNUMvector<double> arctg (1, nFrequencyPoints);
autoNUMvector<double> al2 (1, nFrequencyPoints);
Melder_assert (frame->nx >= nx);
Melder_assert (hamming->nx == nx);
// Compute the absolute value of the globally largest amplitude w.r.t. the global mean.
double globalMean, globalPeak;
Sound_localMean (sound.peek(), sound -> xmin, sound -> xmax, &globalMean);
Sound_localPeak (sound.peek(), sound -> xmin, sound -> xmax, globalMean, &globalPeak);
/*
For the cubic spline interpolation we need the frequencies on an octave
scale, i.e., a log2 scale. All frequencies must be DIFFERENT, otherwise
the cubic spline interpolation will give corrupt results.
Because log2(f==0) is not defined, we use the heuristic: f[2]-f[1] == f[3]-f[2].
*/
for (long i = 2; i <= nfft2; i++) {
fl2[i] = NUMlog2 ( (i - 1) * df);
}
fl2[1] = 2 * fl2[2] - fl2[3];
// Calculate frequencies regularly spaced on a log2-scale and
// the frequency weighting function.
for (long i = 1; i <= nFrequencyPoints; i++) {
arctg[i] = 0.5 + atan (3 * (i - atans) / nPointsPerOctave) / NUMpi;
}
// Perform the analysis on all frames.
for (long i = 1; i <= numberOfFrames; i++) {
Pitch_Frame pitchFrame = &thy frame[i];
double hm = 1, f0, pitch_strength, localMean, localPeak;
double tmid = Sampled_indexToX (thee.peek(), i); /* The center of this frame */
long nx_tmp = frame -> nx;
// Copy a frame from the sound, apply a hamming window. Get local 'intensity'
frame -> nx = nx; /*begin vies */
Sound_into_Sound (sound.peek(), frame.peek(), tmid - halfWindow);
Sounds_multiply (frame.peek(), hamming.peek());
Sound_localMean (sound.peek(), tmid - 3 * halfWindow, tmid + 3 * halfWindow, &localMean);
Sound_localPeak (sound.peek(), tmid - halfWindow, tmid + halfWindow, localMean, &localPeak);
pitchFrame -> intensity = localPeak > globalPeak ? 1 : localPeak / globalPeak;
frame -> nx = nx_tmp; /* einde vies */
// Get the Fourier spectrum.
autoSpectrum spec = Sound_to_Spectrum (frame.peek(), 1);
Melder_assert (spec->nx == nfft2);
// From complex spectrum to amplitude spectrum.
for (long j = 1; j <= nfft2; j++) {
double rs = spec -> z[1][j], is = spec -> z[2][j];
specAmp[j] = sqrt (rs * rs + is * is);
}
// Enhance the peaks in the spectrum.
spec_enhance_SHS (specAmp.peek(), nfft2);
// Smooth the enhanced spectrum.
spec_smoooth_SHS (specAmp.peek(), nfft2);
// Go to a logarithmic scale and perform cubic spline interpolation to get
// spectral values for the increased number of frequency points.
NUMspline (fl2.peek(), specAmp.peek(), nfft2, 1e30, 1e30, yv2.peek());
for (long j = 1; j <= nFrequencyPoints; j++) {
double f = fminl2 + (j - 1) * dfl2;
NUMsplint (fl2.peek(), specAmp.peek(), yv2.peek(), nfft2, f, &al2[j]);
}
// Multiply by frequency selectivity of the auditory system.
for (long j = 1; j <= nFrequencyPoints; j++) al2[j] = al2[j] > 0 ?
al2[j] * arctg[j] : 0;
// The subharmonic summation. Shift spectra in octaves and sum.
Pitch_Frame_init (pitchFrame, maxnCandidates);
autoNUMvector<double> sumspec (1, nFrequencyPoints);
pitchFrame -> nCandidates = 0; /* !!!!! */
for (long m = 1; m <= maxnSubharmonics + 1; m++) {
long kb = 1 + (long) floor (nPointsPerOctave * NUMlog2 (m));
for (long k = kb; k <= nFrequencyPoints; k++) {
sumspec[k - kb + 1] += al2[k] * hm;
}
hm *= compressionFactor;
}
// First register the voiceless candidate (always present).
Pitch_Frame_addPitch (pitchFrame, 0, 0, maxnCandidates);
/*
Get the best local estimates for the pitch as the maxima of the
subharmonic sum spectrum by parabolic interpolation on three points:
The formula for a parabole with a maximum is:
y(x) = a - b (x - c)^2 with a, b, c >= 0
The three points are (-x, y1), (0, y2) and (x, y3).
The solution for a (the maximum) and c (the position) is:
a = (2 y1 (4 y2 + y3) - y1^2 - (y3 - 4 y2)^2)/( 8 (y1 - 2 y2 + y3)
c = dx (y1 - y3) / (2 (y1 - 2 y2 + y3))
(b = (2 y2 - y1 - y3) / (2 dx^2) )
*/
for (long k = 2; k <= nFrequencyPoints - 1; k++) {
double y1 = sumspec[k - 1], y2 = sumspec[k], y3 = sumspec[k + 1];
if (y2 > y1 && y2 >= y3) {
double denum = y1 - 2 * y2 + y3, tmp = y3 - 4 * y2;
double x = dfl2 * (y1 - y3) / (2 * denum);
double f = pow (2, fminl2 + (k - 1) * dfl2 + x);
double strength = (2 * y1 * (4 * y2 + y3) - y1 * y1 - tmp * tmp) / (8 * denum);
Pitch_Frame_addPitch (pitchFrame, f, strength, maxnCandidates);
}
}
/*
Check whether f0 corresponds to an actual periodicity T = 1 / f0:
correlate two signal periods of duration T, one starting at the
middle of the interval and one starting T seconds before.
If there is periodicity the correlation coefficient should be high.
However, some sounds do not show any regularity, or very low
frequency and regularity, and nevertheless have a definite
pitch, e.g. Shepard sounds.
*/
Pitch_Frame_getPitch (pitchFrame, &f0, &pitch_strength);
if (f0 > 0) {
cc[i] = Sound_correlateParts (sound.peek(), tmid - 1.0 / f0, tmid, 1.0 / f0);
}
}
// Base V/UV decision on correlation coefficients.
// Resize the pitch strengths w.r.t. the cc.
double vuvCriterium = 0.52;
for (long i = 1; i <= numberOfFrames; i++) {
Pitch_Frame_resizeStrengths (& thy frame[i], cc[i], vuvCriterium);
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, U": no Pitch (shs) created.");
}
}
PowerCepstrogram Sound_to_PowerCepstrogram_hillenbrand (Sound me, double minimumPitch, double dt) {
try {
// minimum analysis window has 3 periods of lowest pitch
double analysisWidth = 3 / minimumPitch;
if (analysisWidth > my dx * my nx) {
analysisWidth = my dx * my nx;
}
double t1, samplingFrequency = 1 / my dx;
autoSound thee;
if (samplingFrequency > 30000) {
samplingFrequency = samplingFrequency / 2;
thee.reset (Sound_resample (me, samplingFrequency, 1));
} else {
thee.reset (Data_copy (me));
}
// pre-emphasis with fixed coefficient 0.9
for (long i = thy nx; i > 1; i--) {
thy z[1][i] -= 0.9 * thy z[1][i - 1];
}
long nosInWindow = analysisWidth * samplingFrequency, nFrames;
if (nosInWindow < 8) {
Melder_throw ("Analysis window too short.");
}
Sampled_shortTermAnalysis (thee.peek(), analysisWidth, dt, & nFrames, & t1);
autoNUMvector<double> hamming (1, nosInWindow);
for (long i = 1; i <= nosInWindow; i++) {
hamming[i] = 0.54 -0.46 * cos(2 * NUMpi * (i - 1) / (nosInWindow - 1));
}
long nfft = 8; // minimum possible
while (nfft < nosInWindow) { nfft *= 2; }
long nfftdiv2 = nfft / 2;
autoNUMvector<double> fftbuf (1, nfft); // "complex" array
autoNUMvector<double> spectrum (1, nfftdiv2 + 1); // +1 needed
autoNUMfft_Table fftTable;
NUMfft_Table_init (&fftTable, nfft); // sound to spectrum
double qmax = 0.5 * nfft / samplingFrequency, dq = qmax / (nfftdiv2 + 1);
autoPowerCepstrogram him = PowerCepstrogram_create (my xmin, my xmax, nFrames, dt, t1, 0, qmax, nfftdiv2+1, dq, 0);
autoMelderProgress progress (L"Cepstrogram analysis");
for (long iframe = 1; iframe <= nFrames; iframe++) {
double tbegin = t1 + (iframe - 1) * dt - analysisWidth / 2;
tbegin = tbegin < thy xmin ? thy xmin : tbegin;
long istart = Sampled_xToIndex (thee.peek(), tbegin);
istart = istart < 1 ? 1 : istart;
long iend = istart + nosInWindow - 1;
iend = iend > thy nx ? thy nx : iend;
for (long i = 1; i <= nosInWindow; i++) {
fftbuf[i] = thy z[1][istart + i - 1] * hamming[i];
}
for (long i = nosInWindow + 1; i <= nfft; i++) {
fftbuf[i] = 0;
}
NUMfft_forward (&fftTable, fftbuf.peek());
complexfftoutput_to_power (fftbuf.peek(), nfft, spectrum.peek(), true); // log10(|fft|^2)
// subtract average
double specmean = spectrum[1];
for (long i = 2; i <= nfftdiv2 + 1; i++) {
specmean += spectrum[i];
}
specmean /= nfftdiv2 + 1;
for (long i = 1; i <= nfftdiv2 + 1; i++) {
spectrum[i] -= specmean;
}
/*
* Here we diverge from Hillenbrand as he takes the fft of half of the spectral values.
* H. forgets that the actual spectrum has nfft/2+1 values. Thefore, we take the inverse
* transform because this keeps the number of samples a power of 2.
* At the same time this results in twice as much numbers in the quefrency domain, i.e. we end with nfft/2+1
* numbers while H. has only nfft/4!
*/
fftbuf[1] = spectrum[1];
for (long i = 2; i < nfftdiv2 + 1; i++) {
fftbuf[i+i-2] = spectrum[i];
fftbuf[i+i-1] = 0;
}
fftbuf[nfft] = spectrum[nfftdiv2 + 1];
NUMfft_backward (&fftTable, fftbuf.peek());
for (long i = 1; i <= nfftdiv2 + 1; i++) {
his z[i][iframe] = fftbuf[i] * fftbuf[i];
}
if ((iframe % 10) == 1) {
Melder_progress ((double) iframe / nFrames, L"Cepstrogram analysis of frame ",
Melder_integer (iframe), L" out of ", Melder_integer (nFrames), L".");
}
}
return him.transfer();
} catch (MelderError) {
Melder_throw (me, ": no Cepstrogram created.");
}
}
autoSpectrogram Sound_and_Pitch_to_Spectrogram (Sound me, Pitch thee, double analysisWidth, double dt, double f1_hz, double fmax_hz, double df_hz, double relative_bw) {
try {
double t1, windowDuration = 2.0 * analysisWidth; /* gaussian window */
double nyquist = 0.5 / my dx, samplingFrequency = 2.0 * nyquist, fmin_hz = 0.0;
long numberOfFrames, f0_undefined = 0.0;
if (my xmin > thy xmin || my xmax > thy xmax) Melder_throw
(U"The domain of the Sound is not included in the domain of the Pitch.");
double f0_median = Pitch_getQuantile (thee, thy xmin, thy xmax, 0.5, kPitch_unit_HERTZ);
if (f0_median == NUMundefined || f0_median == 0.0) {
f0_median = 100.0;
Melder_warning (U"Pitch values undefined. Bandwith fixed to 100 Hz. ");
}
if (f1_hz <= 0.0) {
f1_hz = 100.0;
}
if (fmax_hz <= 0.0) {
fmax_hz = nyquist;
}
if (df_hz <= 0.0) {
df_hz = f0_median / 2.0;
}
if (relative_bw <= 0.0) {
relative_bw = 1.1;
}
fmax_hz = MIN (fmax_hz, nyquist);
long numberOfFilters = lround ( (fmax_hz - f1_hz) / df_hz);
Sampled_shortTermAnalysis (me, windowDuration, dt, &numberOfFrames, &t1);
autoSpectrogram him = Spectrogram_create (my xmin, my xmax, numberOfFrames, dt, t1, fmin_hz, fmax_hz, numberOfFilters, df_hz, f1_hz);
// Temporary objects
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoMelderProgress progress (U"Sound & Pitch: To FormantFilter");
for (long iframe = 1; iframe <= numberOfFrames; iframe++) {
double t = Sampled_indexToX (him.get(), iframe);
double b, f0 = Pitch_getValueAtTime (thee, t, kPitch_unit_HERTZ, 0);
if (f0 == NUMundefined || f0 == 0.0) {
f0_undefined ++;
f0 = f0_median;
}
b = relative_bw * f0;
Sound_into_Sound (me, sframe.get(), t - windowDuration / 2.0);
Sounds_multiply (sframe.get(), window.get());
Sound_into_Spectrogram_frame (sframe.get(), him.get(), iframe, b);
if (iframe % 10 == 1) {
Melder_progress ( (double) iframe / numberOfFrames, U"Frame ", iframe, U" out of ",
numberOfFrames, U".");
}
}
_Spectrogram_windowCorrection (him.get(), window -> nx);
return him;
} catch (MelderError) {
Melder_throw (U"FormantFilter not created from Pitch & FormantFilter.");
}
}
BarkFilter Sound_to_BarkFilter (Sound me, double analysisWidth, double dt,
double f1_bark, double fmax_bark, double df_bark) {
try {
double t1, nyquist = 0.5 / my dx, samplingFrequency = 2 * nyquist;
double windowDuration = 2 * analysisWidth; /* gaussian window */
double zmax = NUMhertzToBark2 (nyquist);
double fmin_bark = 0;
long nt, frameErrorCount = 0;
// Check defaults.
if (f1_bark <= 0) {
f1_bark = 1;
}
if (fmax_bark <= 0) {
fmax_bark = zmax;
}
if (df_bark <= 0) {
df_bark = 1;
}
fmax_bark = MIN (fmax_bark, zmax);
long nf = floor ( (fmax_bark - f1_bark) / df_bark + 0.5);
if (nf <= 0) {
Melder_throw ("The combination of filter parameters is not valid.");
}
Sampled_shortTermAnalysis (me, windowDuration, dt, & nt, & t1);
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoBarkFilter thee = BarkFilter_create (my xmin, my xmax, nt, dt, t1,
fmin_bark, fmax_bark, nf, df_bark, f1_bark);
autoMelderProgress progess (L"BarkFilter analysis");
for (long i = 1; i <= nt; i++) {
double t = Sampled_indexToX (thee.peek(), i);
Sound_into_Sound (me, sframe.peek(), t - windowDuration / 2);
Sounds_multiply (sframe.peek(), window.peek());
if (! Sound_into_BarkFilter_frame (sframe.peek(), thee.peek(), i)) {
frameErrorCount++;
}
if ( (i % 10) == 1) {
Melder_progress ( (double) i / nt, L"BarkFilter analysis: frame ",
Melder_integer (i), L" from ", Melder_integer (nt), L"."); therror
}
}
if (frameErrorCount > 0) {
Melder_warning (L"Analysis results of ", Melder_integer (frameErrorCount), L" frame(s) out of ",
Melder_integer (nt), L" will be suspect.");
}
double ref = FilterBank_DBREF * gaussian_window_squared_correction (window -> nx);
NUMdmatrix_to_dBs (thy z, 1, thy ny, 1, thy nx, ref, FilterBank_DBFAC, FilterBank_DBFLOOR);
return thee.transfer();
} catch (MelderError) {
SPINET Sound_to_SPINET (Sound me, double timeStep, double windowDuration,
double minimumFrequencyHz, double maximumFrequencyHz, long nFilters,
double excitationErbProportion, double inhibitionErbProportion) {
try {
double firstTime, b = 1.02, samplingFrequency = 1 / my dx;
if (timeStep < my dx) {
timeStep = my dx;
}
if (maximumFrequencyHz > samplingFrequency / 2) {
maximumFrequencyHz = samplingFrequency / 2;
}
long numberOfFrames;
Sampled_shortTermAnalysis (me, windowDuration, timeStep, &numberOfFrames, &firstTime);
autoSPINET thee = SPINET_create (my xmin, my xmax, numberOfFrames, timeStep, firstTime,
minimumFrequencyHz, maximumFrequencyHz, nFilters, excitationErbProportion, inhibitionErbProportion);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoSound frame = Sound_createSimple (1, windowDuration, samplingFrequency);
autoNUMvector<double> f (1, nFilters);
autoNUMvector<double> bw (1, nFilters);
autoNUMvector<double> aex (1, nFilters);
autoNUMvector<double> ain (1, nFilters);
// Cochlear filterbank: gammatone
for (long i = 1; i <= nFilters; i++) {
f[i] = NUMerbToHertz (thy y1 + (i - 1) * thy dy);
bw[i] = 2 * NUMpi * b * (f[i] * (6.23e-6 * f[i] + 93.39e-3) + 28.52);
}
autoMelderProgress progress (L"SPINET analysis");
for (long i = 1; i <= nFilters; i++) {
double bb = (f[i] / 1000) * exp (- f[i] / 1000); // outer & middle ear and phase locking
double tgammaMax = (thy gamma - 1) / bw[i]; // Time where gammafunction envelope has maximum
double gammaMaxAmplitude = pow ( (thy gamma - 1) / (NUMe * bw[i]), (thy gamma - 1)); // tgammaMax
double timeCorrection = tgammaMax - windowDuration / 2;
autoSound gammaTone = Sound_createGammaTone (0, 0.1, samplingFrequency,
thy gamma, b, f[i], 0, 0, 0);
autoSound filtered = Sounds_convolve (me, gammaTone.peek(), kSounds_convolve_scaling_SUM, kSounds_convolve_signalOutsideTimeDomain_ZERO);
// To energy measure: weigh with broad-band transfer function
for (long j = 1; j <= numberOfFrames; j++) {
Sound_into_Sound (filtered.peek(), frame.peek(), Sampled_indexToX (thee.peek(), j) + timeCorrection);
Sounds_multiply (frame.peek(), window.peek());
thy y[i][j] = Sound_power (frame.peek()) * bb / gammaMaxAmplitude;
}
Melder_progress ( (double) i / nFilters, L"SPINET: filter ", Melder_integer (i), L" from ",
Melder_integer (nFilters), L".");
}
// Excitatory and inhibitory area functions
for (long i = 1; i <= nFilters; i++) {
for (long k = 1; k <= nFilters; k++) {
double fr = (f[k] - f[i]) / bw[i];
aex[i] += fgamma (fr / thy excitationErbProportion, thy gamma);
ain[i] += fgamma (fr / thy inhibitionErbProportion, thy gamma);
}
}
// On-center off-surround interactions
for (long j = 1; j <= numberOfFrames; j++)
for (long i = 1; i <= nFilters; i++) {
double a = 0;
for (long k = 1; k <= nFilters; k++) {
double fr = (f[k] - f[i]) / bw[i];
double hexsq = fgamma (fr / thy excitationErbProportion, thy gamma);
double hinsq = fgamma (fr / thy inhibitionErbProportion, thy gamma);
a += thy y[k][j] * (hexsq / aex[i] - hinsq / ain[i]);
}
thy s[i][j] = a > 0 ? a : 0;
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, ": no SPINET created.");
}
}
