static void *xtract_new(t_symbol *me, t_int argc, t_atom *argv) {
t_xtract_tilde *x = (t_xtract_tilde *)pd_new(xtract_class);
xtract_mel_filter *mf;
t_int n, N, M, f, F,
n_args,
type;
t_float *argv_max;
t_symbol *arg1;
xtract_function_descriptor_t *fd;
char *p_name,
*p_desc,
*author;
int year;
p_name = p_desc = author = NULL;
n_args = type = 0;
f = F = XTRACT_FEATURES;
N = BLOCKSIZE;
x->argv = NULL;
x->argv_type = 0;
x->done_init = 0;
x->is_scalar = 0;
x->is_subframe = 0;
x->feature = -1;
/* Allocate data area */
x->data = (double *)getbytes(N * sizeof(double));
x->result = (double *)getbytes(N * sizeof(double));
/* Parse arguments */
if(argc){
arg1 = atom_getsymbol(argv);
if(arg1 == gensym("subframe"))
x->is_subframe = 1;
else
x->feature_name = atom_getsymbol(argv);
}
else {
post("xtract~: No arguments given");
return (void *)x;
}
if(argc > 1){
if(x->is_subframe)
x->feature_name = atom_getsymbol(argv+1);
else
N = atom_getint(argv+1);
}
if(argc > 2)
N = atom_getint(argv+2);
x->init_blocksize = N;
M = N >> 1;
/* get function descriptors */
fd = (xtract_function_descriptor_t *)xtract_make_descriptors();
/* iterate over descriptors */
while(f--){
/* map creation arg to feature */
if(x->feature_name == gensym(fd[f].algo.name)){
x->feature = f;
break;
}
}
if(x->feature == -1)
post("xtract~: feature not found: %s", x->feature_name->s_name);
/* allocate memory for feature arguments */
n_args = fd[f].argc;
type = fd[f].argv.type;
x->argv_type = type;
if(n_args){
for(n = 0; n < n_args; n++){
argv_max = &fd[f].argv.max[n];
/*post("Argument %d, max: %.2f", n, *argv_max); */
}
if(type == XTRACT_MEL_FILTER){
x->memory.argv = (size_t)(n_args * sizeof(xtract_mel_filter));
x->argv = (xtract_mel_filter *)getbytes(x->memory.argv);
}
else if(type == XTRACT_INT){
x->memory.argv = (size_t)(n_args * sizeof(t_int));
x->argv = (t_int *)getbytes(x->memory.argv);
}
else if (type == XTRACT_FLOAT){
x->memory.argv = (size_t)(n_args * sizeof(t_float));
x->argv = (t_float *)getbytes(x->memory.argv);
}
else
x->memory.argv = 0;
}
p_name = fd[f].algo.p_name;
p_desc = fd[f].algo.p_desc;
author = fd[f].algo.author;
year = fd[f].algo.year;
if(argc){
if(strcmp(p_name, ""))
post("xtract~: %s", p_name );
if(strcmp(p_desc, ""))
post("xtract~: %s", p_desc );
if(strcmp(author, "") && year)
post("xtract~: %s(%d)", author, year);
}
/* Adjust frame size if we are using subframe features */
if(x->is_subframe)
N = M;
post("xtract~: window size: %d", N);
/* do init if needed */
if(x->feature == XTRACT_MFCC){
mf = x->argv;
mf->n_filters = 20;
post("xtract~: mfcc: filters = %d",
((xtract_mel_filter *)x->argv)->n_filters);
mf->filters =
(t_float **)getbytes(mf->n_filters * sizeof(t_float *));
for(n = 0; n < mf->n_filters; n++)
mf->filters[n] = (float *)getbytes(N * sizeof(float));
xtract_init_mfcc(N, NYQUIST, XTRACT_EQUAL_GAIN, 80.0f,
18000.0f, mf->n_filters, mf->filters);
x->done_init = 1;
}
else if(x->feature == XTRACT_BARK_COEFFICIENTS){
xtract_init_bark(N, NYQUIST, x->argv);
x->done_init = 1;
}
else if(x->feature == XTRACT_WINDOWED){
x->window = xtract_init_window(N, XTRACT_HANN);
x->argv = x->window;
x->done_init = 1;
}
/* Initialise fft_plan if required */
if(x->feature == XTRACT_AUTOCORRELATION_FFT ||
x->feature == XTRACT_SPECTRUM ||
x->feature == XTRACT_DCT){
xtract_init_fft(N, x->feature);
x->done_init = 1;
}
if(fd[f].is_scalar)
x->is_scalar = 1;
/*
if(x->feature == XTRACT_AUTOCORRELATION ||
x->feature == XTRACT_AUTOCORRELATION_FFT ||
x->feature == XTRACT_MFCC || x->feature == XTRACT_AMDF ||
x->feature == XTRACT_ASDF|| x->feature == XTRACT_DCT ||
x->feature == XTRACT_BARK_COEFFICIENTS ||
x->feature == XTRACT_SPECTRUM ||
x->feature == XTRACT_PEAK_SPECTRUM ||
x->feature == XTRACT_HARMONIC_SPECTRUM ||
x->feature == XTRACT_LPC ||
x->feature == XTRACT_LPCC ||
x->feature == XTRACT_WINDOWED)
x->feature_type = XTRACT_VECTOR;
*/
/* else if (x->feature == XTRACT_FLUX || x->feature == XTRACT_ATTACK_TIME ||
x->feature == XTRACT_DECAY_TIME || x->feature == XTRACT_DIFFERENCE_VECTOR)
x->feature_type = XTRACT_DELTA; */
/*
else x->feature_type = XTRACT_SCALAR;
*/
/* argv through right inlet */
inlet_new(&x->x_obj, &x->x_obj.ob_pd, gensym("list"), gensym("list"));
/* if feature is vector, create signal out */
if(!x->is_scalar)
outlet_new(&x->x_obj, &s_signal);
/* otherwise: float */
else
outlet_new(&x->x_obj, &s_float);
if(x->is_scalar && x->is_subframe)
post(
"xtract~: warning: subframes not yet supported for scalar features");
/* free the function descriptors */
xtract_free_descriptors(fd);
return (void *)x;
}
double* extractall(double* wavetable, long numFrames, double samplerate){
long size = 38+5*BLOCKSIZE+MFCC_FREQ_BANDS;
double *FVec = (double*) malloc(size*sizeof(double));
long count=0;
long i,n,j,k;
int rv = XTRACT_SUCCESS;
xtract_mel_filter mel_filters;
/* get the F0 */
xtract[XTRACT_WAVELET_F0](wavetable, BLOCKSIZE, &samplerate, &f0);
FVec[count++] = f0;
//* get the F0 as a MIDI note
xtract[XTRACT_MIDICENT](NULL, 0, &f0, &midicents);
FVec[count++] = midicents;
//* get the mean of the input
xtract[XTRACT_MEAN](wavetable, BLOCKSIZE, NULL, &mean);
FVec[count++] = mean;
//MY-VARIANCE
xtract[XTRACT_VARIANCE](wavetable, BLOCKSIZE, &mean, &variance);
FVec[count++] = variance;
//MY-STANDARD DEVIATION
xtract[XTRACT_STANDARD_DEVIATION](wavetable, BLOCKSIZE, &variance, &stdDeviation);
FVec[count++] = stdDeviation;
//MY-AVERAGE DEVIATION
xtract[XTRACT_AVERAGE_DEVIATION](wavetable, BLOCKSIZE, &mean, &avgDeviation);
FVec[count++] = avgDeviation;
//MY-SKEWNESS
argd[0] = mean;
argd[1] = stdDeviation;
xtract[XTRACT_SKEWNESS](wavetable, BLOCKSIZE, argd, &skewness);
FVec[count++] = skewness;
//MY-KURTOSIS
xtract[XTRACT_KURTOSIS](wavetable, BLOCKSIZE, argd, &kurtosis);
FVec[count++] = kurtosis;
//MY-ZERO CROSSING RATE
xtract[XTRACT_ZCR](wavetable, BLOCKSIZE, NULL, &zcr);
FVec[count++] = zcr;
//MY-RMS AMPLITUDE
xtract[XTRACT_RMS_AMPLITUDE](wavetable, BLOCKSIZE, NULL, &rmsAmplitude);
FVec[count++] = rmsAmplitude;
//MY-HIGHEST VALUE
xtract[XTRACT_HIGHEST_VALUE](wavetable, BLOCKSIZE, NULL, &highestValue);
FVec[count++] = highestValue;
//MY-SUM
xtract[XTRACT_SUM](wavetable, BLOCKSIZE, NULL, &sum);
FVec[count++] = sum;
//MY-CREST
argd[0] = mean;
argd[1] = highestValue;
xtract[XTRACT_CREST](wavetable, BLOCKSIZE, argd, &crest);
FVec[count++] = crest;
//* get the lowest value in the input
argd[0] = -.5;
rv = xtract[XTRACT_LOWEST_VALUE](wavetable, BLOCKSIZE, argd, &lowest);
FVec[count++] = lowest;
//MY-AMDF
xtract[XTRACT_AMDF](wavetable, BLOCKSIZE, NULL, amdf);
for(k=0;k<BLOCKSIZE;k++)
FVec[i++] = amdf[k];
//MY-ASDF
xtract[XTRACT_ASDF](wavetable, BLOCKSIZE, NULL, asdf);
for(k=0;k<BLOCKSIZE;k++)
FVec[i++] = asdf[k];
//MY-DCT
xtract[XTRACT_DCT](wavetable, BLOCKSIZE, NULL, dct);
for(k=0;k<BLOCKSIZE;k++)
FVec[i++] = dct[k];
//* create the window function
window = xtract_init_window(BLOCKSIZE, XTRACT_HANN);
xtract_windowed(wavetable, BLOCKSIZE, window, windowed);
xtract_free_window(window);
//* get the spectrum
argd[0] = SAMPLERATE / (double)BLOCKSIZE;
argd[1] = XTRACT_MAGNITUDE_SPECTRUM;
argd[2] = 0.f; //* DC component - we expect this to zero for square wave
argd[3] = 0.f; //* No Normalisation
xtract_init_fft(BLOCKSIZE, XTRACT_SPECTRUM);
xtract[XTRACT_SPECTRUM](windowed, BLOCKSIZE, &argd[0], spectrum);
xtract_free_fft();
//MY_BARK
// xtract_init_bark(BLOCKSIZE,samplerate,barkCoeff);
// xtract[XTRACT_BARK_COEFFICIENTS](spectrum,BLOCKSIZE,barkCoeff,bark);
//MY-SPECTRAL MEAN
xtract[XTRACT_SPECTRAL_MEAN](spectrum, BLOCKSIZE, NULL, &specMean);
FVec[count++] = specMean;
//MY-SPECTRAL VARIANCE
xtract[XTRACT_SPECTRAL_VARIANCE](spectrum, BLOCKSIZE, &specMean, &specVariance);
FVec[count++] = specVariance;
//MY-SPECTRAL STANDARD DEVIATION
xtract[XTRACT_SPECTRAL_STANDARD_DEVIATION](spectrum, BLOCKSIZE, &specMean, &specStdDeviation);
FVec[count++] = specStdDeviation;
//xtract[XTRACT_SPECTRAL_AVERAGE_DEVIATION](spectrum, BLOCKSIZE, &specMean, &specAvgDeviation);
//FVec[count++] = specAvgDeviation;
//MY-SPECTRAL SKEWNESS
argd[0] = specMean;
argd[1] = specStdDeviation;
xtract[XTRACT_SPECTRAL_SKEWNESS](spectrum, BLOCKSIZE, argd, &specSkewness);
FVec[count++] = specSkewness;
//MY-SPECTRAL KURTOSIS
xtract[XTRACT_SPECTRAL_KURTOSIS](spectrum, BLOCKSIZE, argd, &specKurtosis);
FVec[count++] = specKurtosis;
xtract[XTRACT_SPECTRAL_CENTROID](spectrum, BLOCKSIZE, NULL, ¢roid);
FVec[count++] = centroid;
//MY-POWER
xtract[XTRACT_POWER](spectrum, BLOCKSIZE, NULL, &power);
FVec[count++] = power;
//MY-SHARPNESS
xtract[XTRACT_SHARPNESS](spectrum, BLOCKSIZE, NULL, &sharpness);
FVec[count++] = sharpness;
//MY-SPECTRAL SLOPE
xtract[XTRACT_SPECTRAL_SLOPE](spectrum, BLOCKSIZE/2, NULL, &specSlope);
FVec[count++] = specSlope;
//MY-HPS
xtract[XTRACT_HPS](spectrum, BLOCKSIZE, NULL, &hps);
FVec[count++] = hps;
//MY-FLATNESS
xtract[XTRACT_FLATNESS](spectrum, BLOCKSIZE, NULL, &flatness);
FVec[count++] = flatness;
//MY-TONALITY
xtract[XTRACT_TONALITY](NULL, NULL, &flatness, &tonality);
FVec[count++] = tonality;
argd[0] = SAMPLERATE / (double)BLOCKSIZE;
argd[1] = 10.0; //* peak threshold as % of maximum peak
xtract[XTRACT_PEAK_SPECTRUM](spectrum, BLOCKSIZE / 2, argd, peaks);
argd[0] = f0;
argd[1] = .3; //* harmonic threshold
xtract[XTRACT_HARMONIC_SPECTRUM](peaks, BLOCKSIZE, argd, harmonics);
//MY-ODD EVEN RATIO
xtract[XTRACT_ODD_EVEN_RATIO](harmonics, BLOCKSIZE, NULL, &oddEvenRatio);
FVec[count++] = oddEvenRatio;
//MY-TRISTIMULUS-1
xtract[XTRACT_TRISTIMULUS_1](harmonics, BLOCKSIZE, NULL, &tristimulus1);
FVec[count++] = tristimulus1;
//MY-TRISTIMULUS-2
xtract[XTRACT_TRISTIMULUS_2](harmonics, BLOCKSIZE, NULL, &tristimulus2);
FVec[count++] = tristimulus2;
//MY-TRISTIMULUS-2
xtract[XTRACT_TRISTIMULUS_3](harmonics, BLOCKSIZE, NULL, &tristimulus3);
FVec[count++] = tristimulus3;
//MY-NOISINESS
xtract[XTRACT_NOISINESS](NULL, NULL, harmonics, &noisiness);
FVec[count++] = noisiness;
//MY-SPECTRAL INHARMONICITY
// xtract[XTRACT_SPECTRAL_INHARMONICITY](peaks, BLOCKSIZE, &f0, &specInharmonicity);
// FVec[count++] = specInharmonicity;
//MY-SPERAD
// xtract[XTRACT_SPREAD](spectrum, BLOCKSIZE, NULL, &spread);
// FVec[count++] = spread;
//MY-AUTOCORRELATION and AUTOCORRELATION FFT
// xtract[XTRACT_AUTOCORRELATION](wavetable, BLOCKSIZE, NULL, &autocorrelation);
// FVec[count++] = autocorrelation;
// printf("\nAutocorrelation : %f\n", autocorrelation);
//xtract[XTRACT_AUTOCORRELATION_FFT](wavetable, BLOCKSIZE, NULL, &autocorrelationFFT);
//FVec[count++] = autocorrelationFFT;
for(k=0;k<BLOCKSIZE;k++)
FVec[i++] = spectrum[k];
//* compute the MFCCs
mel_filters.n_filters = MFCC_FREQ_BANDS;
mel_filters.filters = (double **)malloc(MFCC_FREQ_BANDS * sizeof(double *));
for(n = 0; n < MFCC_FREQ_BANDS; ++n)
{
mel_filters.filters[n] = (double *)malloc(BLOCKSIZE * sizeof(double));
}
xtract_init_mfcc(BLOCKSIZE >> 1, SAMPLERATE >> 1, XTRACT_EQUAL_GAIN, MFCC_FREQ_MIN, MFCC_FREQ_MAX, mel_filters.n_filters, mel_filters.filters);
xtract_mfcc(spectrum, BLOCKSIZE >> 1, &mel_filters, mfccs);
for(k=0;k<MFCC_FREQ_BANDS;k++)
FVec[i++] = mfccs[k];
//* compute Spectral Flux
argd[0] = SAMPLERATE / (BLOCKSIZE/2);
argd[1] = XTRACT_MAGNITUDE_SPECTRUM;
argd[2] = 0.f; //* DC component
argd[3] = 0.f; //* No Normalisation
xtract_init_fft(BLOCKSIZE/2, XTRACT_SPECTRUM);
xtract_features_from_subframes(wavetable, BLOCKSIZE, XTRACT_SPECTRUM, argd, subframes);
xtract_difference_vector(subframes, BLOCKSIZE, NULL, difference);
argd[0] = 1.0; //* norm order
argd[1] = XTRACT_POSITIVE_SLOPE; //* positive slope
xtract_flux(difference, BLOCKSIZE/2, argd, &flux);
FVec[i++] = flux;
for(k=0;k<BLOCKSIZE;k++)
FVec[i++] = peaks[k];
/*
printf("\nF0: %f\n", f0);
printf("\nMIDI cents: %f\n", midicents);
printf("\nInput mean = %.5f\t%.5f\n", mean); //* We expect this to be zero for a square wave
printf("\nVariance = %f\n",variance);
printf("\nStandard Deviation = %f\n",stdDeviation);
printf("\nAverage Deviation = %f\n",avgDeviation);
printf("\nSkewness = %f\n",skewness);
printf("\nKurtosis = %f\n",kurtosis);
printf("\nZero Crossing Rate = %f\n",zcr);
printf("\nRMS Amplitude = %f\n",rmsAmplitude);
printf("\nHighest Value = %f\n",highestValue);
printf("\nSum = %f\n", sum);
printf("\nCrest = %f\n", crest);
printf("\nLowest value = %.6f\n\n", lowest);
for(n=0;n<BLOCKSIZE;n+=50)
printf("\nAMDF[%d]: %f",n,amdf[n]);
for(n=0;n<BLOCKSIZE;n+=50)
printf("\nASDF[%d]: %f",n,asdf[n]);
for(n=0;n<BLOCKSIZE;n+=50)
printf("\nDCT[%d]: %f",n,dct[n]);
// for(n=0;n<BLOCKSIZE;n+=1000)
// printf("\nBARK[%d]: %f",n,bark[n]);
printf("\nSpectral Mean: %f\n", specMean);
printf("\nSpectral Variance: %f\n", specVariance);
printf("\nSpectral Standard Deviation: %f\n", specStdDeviation);
//printf("\nSpectral Average Deviation: %f\n", specAvgDeviation);
printf("\nSpectral Skewness: %f\n", specSkewness);
printf("\nSpectral Kurtosis: %f\n", specKurtosis);
printf("\nSpectral Centroid: %f\n", centroid);
printf("\nPower = %f\n",power);
printf("\nSharpness = %f\n",sharpness);
printf("\nSpectral Slope = %f\n",specSlope);
printf("\nHPS = %f\n",hps);
printf("\nFlatness: %f\n", flatness);
printf("\nTonality: %f\n", tonality);
printf("\nOdd Even Ratio : %f\n", oddEvenRatio);
printf("\nTristimulus-1 : %f\n", tristimulus1);
printf("\nTristimulus-2 : %f\n", tristimulus2);
printf("\nTristimulus-3 : %f\n", tristimulus3);
printf("\nNoisiness : %f\n", noisiness);
// printf("\nSpectral Inharmonicity : %f\n", specInharmonicity);
// printf("\nSpread : %f\n", spread);
// printf("\nAutocorrelation fft : %f\n",autocorrelationFFT);
printf("\nSpectrum:\n");
for(n = 0; n < (BLOCKSIZE >> 1); n+=1000)
{
printf("freq: %.1f\tamp: %.6f", spectrum[n + (BLOCKSIZE >> 1)], spectrum[n]);
if (peaks[n + (BLOCKSIZE >> 1)] != 0.f)
{
printf("\tpeak:: freq: %.1f\tamp: %.6f\n", peaks[n + (BLOCKSIZE >> 1)], peaks[n]);
}
else
{
printf("\n");
}
}
printf("\n");
printf("MFCCs:\n");
for(n = 0; n < MFCC_FREQ_BANDS; ++n)
{
printf("band: %d\t", n);
if(n < 10) {
printf("\t");
}
printf("coeff: %f\n", mfccs[n]);
}
printf("Flux: %f\n", flux);*/
//}
// cleanup
for(n = 0; n < MFCC_FREQ_BANDS; ++n)
{
free(mel_filters.filters[n]);
}
free(mel_filters.filters);
return FVec;
}
