Limiting scipy.signal.spectrogram to calculate only specific frequencies

Question

I am following the tutorial example at scipy.signal.spectrogram. Is it possible to limit the frequencies we want to calculate the spectogram for, let's say in the range 1000-2000Hz. As per FFT, we will get it for half of the sampling frequencies. If not then can I slice the outputs to get a 3D matrix of t,f,Sxx where I limit the frequencies to say 1000-2000 Hz? Usage in my case is that my signal contains only certain range of frequencies and keeping all will be costly for my analysis.

Answer 1

Unless you were to analyse a very small percentage of the frequency spectrum, computing the entire spectrum with the FFT (and throwing away the parts you don't want) is usually still a fairly efficient way to obtain a partial spectrum.

You can then find the frequencies of interest in f using numpy.where (following this answer by askewchan). The next step would then be to simply slice the output, extracting the desired frequency values in the 1D-array f and associated power spectrum values in the 2D-array Sxx. This can be done as follows:

...
f, t, Sxx = signal.spectrogram(x, fs)

fmin = 1000 # Hz
fmax = 2000 # Hz
freq_slice = np.where((f >= fmin) & (f <= fmax))

# keep only frequencies of interest
f   = f[freq_slice]
Sxx = Sxx[freq_slice,:][0]