Hi, I just came into this repo because I needed to port an MFCC calculation from librosa to java. I found your class very useful, although I had a minimal problem regarding window size.
As my pretrained models did not have default win size (n_fft), I did a minor change to the audio extraction feature in order to make this work the same as original librosa, producing the same results.
I simply want to share this minor tweak if someone needs this in the future:
// Marcos not default window size
private final static int n_win = 1600;
...
private double[] getWindow(){
//Return a Hann window for even n_fft.
//The Hann window is a taper formed by using a raised cosine or sine-squared
//with ends that touch zero.
double[] win = new double[/*n_fft*/ n_win];
for (int i = 0; i < /*n_fft*/n_win; i++){
win[i] = 0.5 - 0.5 * Math.cos(2.0*Math.PI*i/(/*n_fft*/n_win));
}
// Marcos: Pad center win to n_ftt (see librosa spectrum.py)
if (n_win < n_fft) {
double[] padded_win = new double[n_fft];
int lpad = (n_fft - n_win) / 2;
int rpad = n_fft - n_win - lpad;
for (int l=0;l<lpad;l++)
padded_win[l] = 0.0;
for (int m=0;m<n_win;m++)
padded_win[lpad+m] = win[m];
for (int r=0;r<rpad;r++)
padded_win[lpad+n_win+r] = 0.0;
return padded_win;
}
else return win;
}
Hi, I just came into this repo because I needed to port an MFCC calculation from librosa to java. I found your class very useful, although I had a minimal problem regarding window size. As my pretrained models did not have default win size (n_fft), I did a minor change to the audio extraction feature in order to make this work the same as original librosa, producing the same results. I simply want to share this minor tweak if someone needs this in the future: