I asked this over on StackOverflow first but figured you fine people might know more about signal processing (duh).
I'm trying to calculate the spectrogram of an audio file in python. There's a nice library function for doing this in scipy.signal.stft(), but it's not behaving as I expect it to.
Since a spectrogram is just a Short-time Fourier Transform, I expect the library function to chop my audio file up into segments ('time frames'), and then do a Fourier transform on each time frame.
This is what the function does as far as I can tell, but the size of the output matrix is behaving strangely. I'll explain:
The parameters you can specify for the function is
noverlap, which are the number of audio samples in each time frame and how much each time frame should overlap with the previous respectively. From this, we can caluclate the hop size, i.e. how many samples should be between each time frame:
H = nperseg - noverlap.
This is where the strangeness comes in. The number of frequencies 'measured' in the Fourier transform of each time frame is exactly equal to the hop size, and it's always evenly spaced between 0 Hz and half the audio sampling frequency.
Say I have an audio file sampled at 22 kHz and I've set the function parameters so that the hop size is, say, 5 audio samples between each time frame. Then the output will show 5 different frequencies evenly spaced between 0-11 kHz, like this:
[0kHz 2.75kHz 5.5kHz 8.75kHz 11kHz].
Why only these 5? What if I want to have a hop size of 5 but also see what the Fourier transform looks like for more frequencies? What if I want to have a hop size of 706 samples and 149 measured frequencies between 50 kHz and 65 kHz? Is this impossible?