Sound Sound_resample (Sound me, double samplingFrequency, long precision) {
double upfactor = samplingFrequency * my dx;
if (fabs (upfactor - 2) < 1e-6) return Sound_upsample (me);
if (fabs (upfactor - 1) < 1e-6) return Data_copy (me);
try {
long numberOfSamples = lround ((my xmax - my xmin) * samplingFrequency);
if (numberOfSamples < 1)
Melder_throw (U"The resampled Sound would have no samples.");
autoSound filtered = NULL;
if (upfactor < 1.0) { // need anti-aliasing filter?
long nfft = 1, antiTurnAround = 1000;
while (nfft < my nx + antiTurnAround * 2) nfft *= 2;
autoNUMvector <double> data (1, nfft);
filtered.reset (Sound_create (my ny, my xmin, my xmax, my nx, my dx, my x1));
for (long channel = 1; channel <= my ny; channel ++) {
for (long i = 1; i <= nfft; i ++) {
data [i] = 0;
}
NUMvector_copyElements (my z [channel], & data [antiTurnAround], 1, my nx);
NUMrealft (data.peek(), nfft, 1); // go to the frequency domain
for (long i = (long) floor (upfactor * nfft); i <= nfft; i ++) {
data [i] = 0; // filter away high frequencies
}
data [2] = 0.0;
NUMrealft (data.peek(), nfft, -1); // return to the time domain
double factor = 1.0 / nfft;
double *to = filtered -> z [channel];
for (long i = 1; i <= my nx; i ++) {
to [i] = data [i + antiTurnAround] * factor;
}
}
me = filtered.peek(); // reference copy; remove at end
}
autoSound thee = Sound_create (my ny, my xmin, my xmax, numberOfSamples, 1.0 / samplingFrequency,
0.5 * (my xmin + my xmax - (numberOfSamples - 1) / samplingFrequency));
for (long channel = 1; channel <= my ny; channel ++) {
double *from = my z [channel];
double *to = thy z [channel];
if (precision <= 1) {
for (long i = 1; i <= numberOfSamples; i ++) {
double x = Sampled_indexToX (thee.peek(), i);
double index = Sampled_xToIndex (me, x);
long leftSample = (long) floor (index);
double fraction = index - leftSample;
to [i] = leftSample < 1 || leftSample >= my nx ? 0.0 :
(1 - fraction) * from [leftSample] + fraction * from [leftSample + 1];
}
} else {
for (long i = 1; i <= numberOfSamples; i ++) {
double x = Sampled_indexToX (thee.peek(), i);
double index = Sampled_xToIndex (me, x);
to [i] = NUM_interpolate_sinc (my z [channel], my nx, index, precision);
}
}
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, U": not resampled.");
}
}
Sound Sound_upsample (Sound me) {
try {
long nfft = 1;
while (nfft < my nx + 2000) nfft *= 2;
autoSound thee = Sound_create (my ny, my xmin, my xmax, my nx * 2, my dx / 2, my x1 - my dx / 4);
for (long channel = 1; channel <= my ny; channel ++) {
autoNUMvector <double> data (1, 2 * nfft); // zeroing is important...
NUMvector_copyElements (my z [channel], & data [1000], 1, my nx); // ...because this fills only part of the sound
NUMrealft (data.peek(), nfft, 1);
long imin = (long) (nfft * 0.95);
for (long i = imin + 1; i <= nfft; i ++) {
data [i] *= ((double) (nfft - i)) / (nfft - imin);
}
data [2] = 0.0;
NUMrealft (data.peek(), 2 * nfft, -1);
double factor = 1.0 / nfft;
for (long i = 1; i <= thy nx; i ++) {
thy z [channel] [i] = data [i + 2000] * factor;
}
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, U": not upsampled.");
}
}
Sound Sound_resample (Sound me, double samplingFrequency, long precision) {
double *data = NULL;
double upfactor = samplingFrequency * my dx;
long numberOfSamples = floor ((my xmax - my xmin) * samplingFrequency + 0.5), i;
Sound thee = NULL, filtered = NULL;
if (fabs (upfactor - 2) < 1e-6) return Sound_upsample (me);
if (fabs (upfactor - 1) < 1e-6) return (structSound *)Data_copy (me);
if (numberOfSamples < 1)
return (structSound *)Melder_errorp ("Cannot resample to 0 samples.");
thee = Sound_create (my ny, my xmin, my xmax, numberOfSamples, 1.0 / samplingFrequency,
0.5 * (my xmin + my xmax - (numberOfSamples - 1) / samplingFrequency)); cherror
if (upfactor < 1.0) { /* Need anti-aliasing filter? */
long nfft = 1, antiTurnAround = 1000;
while (nfft < my nx + antiTurnAround * 2) nfft *= 2;
data = NUMdvector (1, nfft); cherror
filtered = Sound_create (my ny, my xmin, my xmax, my nx, my dx, my x1); cherror
for (long channel = 1; channel <= my ny; channel ++) {
for (long i = 1; i <= nfft; i ++) {
data [i] = 0;
}
NUMdvector_copyElements (my z [channel], data + antiTurnAround, 1, my nx);
NUMrealft (data, nfft, 1); cherror /* Go to the frequency domain. */
for (long i = floor (upfactor * nfft); i <= nfft; i ++) {
data [i] = 0; /* Filter away high frequencies. */
}
data [2] = 0.0;
NUMrealft (data, nfft, -1); cherror /* Return to the time domain. */
double factor = 1.0 / nfft;
double *to = filtered -> z [channel];
for (long i = 1; i <= my nx; i ++) {
to [i] = data [i + antiTurnAround] * factor;
}
}
me = filtered; /* Reference copy. Remove at end. */
}
Sound Sound_upsample (Sound me) {
double *data = NULL;
Sound thee = NULL;
long nfft = 1;
while (nfft < my nx + 2000) nfft *= 2;
thee = Sound_create (my ny, my xmin, my xmax, my nx * 2, my dx / 2, my x1); cherror
data = NUMdvector (1, 2 * nfft); cherror
for (long channel = 1; channel <= my ny; channel ++) {
NUMdvector_copyElements (my z [channel], data + 1000, 1, my nx);
NUMrealft (data, nfft, 1); cherror
long imin = (long) (nfft * 0.95);
for (long i = imin + 1; i <= nfft; i ++) {
data [i] *= ((double) (nfft - i)) / (nfft - imin);
}
data [2] = 0.0;
NUMrealft (data, 2 * nfft, -1); cherror
double factor = 1.0 / nfft;
for (long i = 1; i <= thy nx; i ++) {
thy z [channel] [i] = data [i + 2000] * factor;
}
}
end:
NUMdvector_free (data, 1);
iferror forget (thee);
return thee;
}
autoSound Spectrum_to_Sound (Spectrum me) {
try {
double *re = my z [1], *im = my z [2];
double lastFrequency = my x1 + (my nx - 1) * my dx;
int originalNumberOfSamplesProbablyOdd = im [my nx] != 0.0 || my xmax - lastFrequency > 0.25 * my dx;
if (my x1 != 0.0)
Melder_throw (U"A Fourier-transformable Spectrum must have a first frequency of 0 Hz, not ", my x1, U" Hz.");
long numberOfSamples = 2 * my nx - ( originalNumberOfSamplesProbablyOdd ? 1 : 2 );
autoSound thee = Sound_createSimple (1, 1 / my dx, numberOfSamples * my dx);
double *amp = thy z [1];
double scaling = my dx;
amp [1] = re [1] * scaling;
for (long i = 2; i < my nx; i ++) {
amp [i + i - 1] = re [i] * scaling;
amp [i + i] = im [i] * scaling;
}
if (originalNumberOfSamplesProbablyOdd) {
amp [numberOfSamples] = re [my nx] * scaling;
if (numberOfSamples > 1) amp [2] = im [my nx] * scaling;
} else {
amp [2] = re [my nx] * scaling;
}
NUMrealft (amp, numberOfSamples, -1);
return thee;
} catch (MelderError) {
Melder_throw (me, U": not converted to Sound.");
}
}
autoSpectrum Spectrum_lpcSmoothing (Spectrum me, int numberOfPeaks, double preemphasisFrequency) {
try {
double gain, a [100];
long numberOfCoefficients = 2 * numberOfPeaks;
autoSound sound = Spectrum_to_Sound (me);
NUMpreemphasize_f (sound -> z [1], sound -> nx, sound -> dx, preemphasisFrequency);
NUMburg (sound -> z [1], sound -> nx, a, numberOfCoefficients, & gain);
for (long i = 1; i <= numberOfCoefficients; i ++) a [i] = - a [i];
autoSpectrum thee = Data_copy (me);
long nfft = 2 * (thy nx - 1);
long ndata = numberOfCoefficients < nfft ? numberOfCoefficients : nfft - 1;
double scale = 10 * (gain > 0 ? sqrt (gain) : 1) / numberOfCoefficients;
autoNUMvector <double> data (1, nfft);
data [1] = 1;
for (long i = 1; i <= ndata; i ++)
data [i + 1] = a [i];
NUMrealft (data.peek(), nfft, 1);
double *re = thy z [1];
double *im = thy z [2];
re [1] = scale / data [1];
im [1] = 0.0;
long halfnfft = nfft / 2;
for (long i = 2; i <= halfnfft; i ++) {
double real = data [i + i - 1], imag = data [i + i];
re [i] = scale / sqrt (real * real + imag * imag) / (1 + thy dx * (i - 1) / preemphasisFrequency);
im [i] = 0;
}
re [halfnfft + 1] = scale / data [2] / (1 + thy dx * halfnfft / preemphasisFrequency);
im [halfnfft + 1] = 0.0;
return thee;
} catch (MelderError) {
Melder_throw (me, U": not smoothed.");
}
}
Sound Sound_autoCorrelate (Sound me, enum kSounds_convolve_scaling scaling, enum kSounds_convolve_signalOutsideTimeDomain signalOutsideTimeDomain) {
try {
long numberOfChannels = my ny, n1 = my nx, n2 = n1 + n1 - 1, nfft = 1;
while (nfft < n2) nfft *= 2;
autoNUMvector <double> data (1, nfft);
double my_xlast = my x1 + (n1 - 1) * my dx;
autoSound thee = Sound_create (numberOfChannels, my xmin - my xmax, my xmax - my xmin, n2, my dx, my x1 - my_xlast);
for (long channel = 1; channel <= numberOfChannels; channel ++) {
double *a = my z [channel];
for (long i = n1; i > 0; i --) data [i] = a [i];
for (long i = n1 + 1; i <= nfft; i ++) data [i] = 0.0;
NUMrealft (data.peek(), nfft, 1);
data [1] *= data [1];
data [2] *= data [2];
for (long i = 3; i <= nfft; i += 2) {
data [i] = data [i] * data [i] + data [i + 1] * data [i + 1];
data [i + 1] = 0.0; // reverse me by taking the conjugate of data1
}
NUMrealft (data.peek(), nfft, -1);
a = thy z [channel];
for (long i = 1; i < n1; i ++) {
a [i] = data [i + (nfft - (n1 - 1))]; // data for the first part ("negative lags") is at the end of data
}
for (long i = 1; i <= n1; i ++) {
a [i + (n1 - 1)] = data [i]; // data for the second part ("positive lags") is at the beginning of data
}
}
switch (signalOutsideTimeDomain) {
case kSounds_convolve_signalOutsideTimeDomain_ZERO: {
// do nothing
} break;
case kSounds_convolve_signalOutsideTimeDomain_SIMILAR: {
for (long channel = 1; channel <= numberOfChannels; channel ++) {
double *a = thy z [channel];
double edge = n1;
for (long i = 1; i < edge; i ++) {
double factor = edge / i;
a [i] *= factor;
a [n2 + 1 - i] *= factor;
}
}
} break;
//case kSounds_convolve_signalOutsideTimeDomain_PERIODIC: {
// do nothing
//} break;
default: Melder_fatal (U"Sounds_autoCorrelate: unimplemented outside-time-domain strategy ", signalOutsideTimeDomain);
}
switch (scaling) {
case kSounds_convolve_scaling_INTEGRAL: {
Vector_multiplyByScalar (thee.peek(), my dx / nfft);
} break;
case kSounds_convolve_scaling_SUM: {
Vector_multiplyByScalar (thee.peek(), 1.0 / nfft);
} break;
case kSounds_convolve_scaling_NORMALIZE: {
double normalizationFactor = Matrix_getNorm (me) * Matrix_getNorm (me);
if (normalizationFactor != 0.0) {
Vector_multiplyByScalar (thee.peek(), 1.0 / nfft / normalizationFactor);
}
} break;
case kSounds_convolve_scaling_PEAK_099: {
Vector_scale (thee.peek(), 0.99);
} break;
default: Melder_fatal (U"Sounds_autoCorrelate: unimplemented scaling ", scaling);
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, U": autocorrelation not computed.");
}
}
Sound Sounds_crossCorrelate (Sound me, Sound thee, enum kSounds_convolve_scaling scaling, enum kSounds_convolve_signalOutsideTimeDomain signalOutsideTimeDomain) {
try {
if (my ny > 1 && thy ny > 1 && my ny != thy ny)
Melder_throw (U"The numbers of channels of the two sounds have to be equal or 1.");
if (my dx != thy dx)
Melder_throw (U"The sampling frequencies of the two sounds have to be equal.");
long numberOfChannels = my ny > thy ny ? my ny : thy ny;
long n1 = my nx, n2 = thy nx;
long n3 = n1 + n2 - 1, nfft = 1;
while (nfft < n3) nfft *= 2;
autoNUMvector <double> data1 (1, nfft);
autoNUMvector <double> data2 (1, nfft);
double my_xlast = my x1 + (n1 - 1) * my dx;
autoSound him = Sound_create (numberOfChannels, thy xmin - my xmax, thy xmax - my xmin, n3, my dx, thy x1 - my_xlast);
for (long channel = 1; channel <= numberOfChannels; channel ++) {
double *a = my z [my ny == 1 ? 1 : channel];
for (long i = n1; i > 0; i --) data1 [i] = a [i];
for (long i = n1 + 1; i <= nfft; i ++) data1 [i] = 0.0;
a = thy z [thy ny == 1 ? 1 : channel];
for (long i = n2; i > 0; i --) data2 [i] = a [i];
for (long i = n2 + 1; i <= nfft; i ++) data2 [i] = 0.0;
NUMrealft (data1.peek(), nfft, 1);
NUMrealft (data2.peek(), nfft, 1);
data2 [1] *= data1 [1];
data2 [2] *= data1 [2];
for (long i = 3; i <= nfft; i += 2) {
double temp = data1 [i] * data2 [i] + data1 [i + 1] * data2 [i + 1]; // reverse me by taking the conjugate of data1
data2 [i + 1] = data1 [i] * data2 [i + 1] - data1 [i + 1] * data2 [i]; // reverse me by taking the conjugate of data1
data2 [i] = temp;
}
NUMrealft (data2.peek(), nfft, -1);
a = him -> z [channel];
for (long i = 1; i < n1; i ++) {
a [i] = data2 [i + (nfft - (n1 - 1))]; // data for the first part ("negative lags") is at the end of data2
}
for (long i = 1; i <= n2; i ++) {
a [i + (n1 - 1)] = data2 [i]; // data for the second part ("positive lags") is at the beginning of data2
}
}
switch (signalOutsideTimeDomain) {
case kSounds_convolve_signalOutsideTimeDomain_ZERO: {
// do nothing
} break;
case kSounds_convolve_signalOutsideTimeDomain_SIMILAR: {
for (long channel = 1; channel <= numberOfChannels; channel ++) {
double *a = his z [channel];
double edge = n1 < n2 ? n1 : n2;
for (long i = 1; i < edge; i ++) {
double factor = edge / i;
a [i] *= factor;
a [n3 + 1 - i] *= factor;
}
}
} break;
//case kSounds_convolve_signalOutsideTimeDomain_PERIODIC: {
// do nothing
//} break;
default: Melder_fatal (U"Sounds_crossCorrelate: unimplemented outside-time-domain strategy ", signalOutsideTimeDomain);
}
switch (scaling) {
case kSounds_convolve_scaling_INTEGRAL: {
Vector_multiplyByScalar (him.peek(), my dx / nfft);
} break;
case kSounds_convolve_scaling_SUM: {
Vector_multiplyByScalar (him.peek(), 1.0 / nfft);
} break;
case kSounds_convolve_scaling_NORMALIZE: {
double normalizationFactor = Matrix_getNorm (me) * Matrix_getNorm (thee);
if (normalizationFactor != 0.0) {
Vector_multiplyByScalar (him.peek(), 1.0 / nfft / normalizationFactor);
}
} break;
case kSounds_convolve_scaling_PEAK_099: {
Vector_scale (him.peek(), 0.99);
} break;
default: Melder_fatal (U"Sounds_crossCorrelate: unimplemented scaling ", scaling);
}
return him.transfer();
} catch (MelderError) {
Melder_throw (me, U" & ", thee, U": not cross-correlated.");
}
}
