python3: speech recognition - calculating features of a WAV file I am supposed to do this as a school project, but I am kind of lost when it comes to signals and would appreciate your help. Also, English is not my first language, and I will be translating the assignment mostly literally, as I am not familiar with a lot of terms used in signal processing, so pardon me for any mistranslations, please.

I have WAV files with these characteristics:


mono
16000 Hz
16-bit precision
16-bit Signed Integer PCM encoding


Right now I am supposed to calculate a vector of features for each frame (sample?) of each WAV file.

They even suggested a solution. They recommended to do this using a linear filter bank, which produces 16 coefficients by summing coefficients of a logarithmic power spectrum. There's an exact formula, but it's irrelevant for this question, I think.

What can I do to get an array with values of this logarithmic power spectrum (is that the right translation)?

I have imported these libraries and can freely use any of its functions: IPython, numpy, matplotlib, soundfile, scipy.signal

Thank you
you can extract most of the well-known features such as MFCC, filter bank, PLP, Prosodic, chroma features and so on ... using:

speechpy or librosa libraries in python environment. You can extract any features from *.wav file. the list of functions is here.you can choose the best of them depending on your need.
for example, you can try for MFCC by this code. also, it is very easy by speechpy functions.
Another great tool for feature extracting is OpenSMILE by AudEERING. You can use it via command line in Windows or terminal in Linux. For more info about it, you can use this link. This tool is most cited in speech research papers.