Comments (2)
Hi!
Regarding the STFT parameters - take a look here at n_fft
and win_length
parameters.
In short, the FFT size can not be smaller than the size of analysis window (so NWaves sets FFT size to 1024 automatically in your case). I guess what you're trying to do is analyze signal in the 1024-samples window and then obtain FFT with lower frequency resolution (256 samples). So essentially, you need to compute 1024-point FFT and then sum values inside each group of 4 adjacent samples. Something like this:
for (int i = 1, k = 0; i < s.Length; i+=4, k++)
low[k] = s[i] + s[i+1] + s[i+2] + s[i+3]
Q1: yes, Y represents power (spectrogram is the sequence of power spectra).
Q2: each power spectrum is normalized by FFT size (by default). Unfortunately, I didn't add the boolean parameter for normalization in Spectrogram()
method (Fft.PowerSpectrum()
has this parameter, though). This parameter will be added in next version of the lib.
Regards,
Tim
from nwaves.
Hi!
Regarding the STFT parameters - take a look here at
n_fft
andwin_length
parameters.In short, the FFT size can not be smaller than the size of analysis window (so NWaves sets FFT size to 1024 automatically in your case). I guess what you're trying to do is analyze signal in the 1024-samples window and then obtain FFT with lower frequency resolution (256 samples). So essentially, you need to compute 1024-point FFT and then sum values inside each group of 4 adjacent samples. Something like this:
for (int i = 1, k = 0; i < s.Length; i+=4, k++) low[k] = s[i] + s[i+1] + s[i+2] + s[i+3]
Q1: yes, Y represents power (spectrogram is the sequence of power spectra).
Q2: each power spectrum is normalized by FFT size (by default). Unfortunately, I didn't add the boolean parameter for normalization inSpectrogram()
method (Fft.PowerSpectrum()
has this parameter, though). This parameter will be added in next version of the lib.Regards,
Tim
Thank you for the detailed reply!
from nwaves.
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