I am trying to implement a deep learning classification solution for (1 second long) audio signals recorded at a very high sample rate, i.e. 2 MHz. All the material and tutorials that I could find work mainly in the human audible range so they are mostly relevant for relatively low sample rates.
Since I am new to audio feature extraction and processing, I cannot wrap my head around all these parameters used in
librosa. I don't have a lot of data either so I want to extract the maximum amount of non-redundant information from these signals.
- n_mfcc, n_mels, n_fft
- hop_length, win_length
In what ratio or relation to one another should these be used? Given the length (1s) and sample_rate (2MHz), what values for these parameters should I start with?
I have tried the often used values for n_mfcc (20, 40), n_mels (32, 64, 128), n_fft (1024, 2048, 4096), hop_length (256, 512, 1024, 2048) etc. but am not sure if I am getting the most out of these signals.
The primary focus is on spectral features like Mel Spectrogram and MFCCs to be used as inputs for the models. Please recommend what the most sensible values for these parameters in this context should be.