I have a time series and generate its spectrogram in Python with matplotlib.pyplot.specgram. After I make some analysis and changes I need to convert the spectrogram back into time series. Is there any function in matplotlib or in other library that I can use directly? Or if not, could you please elaborate on which direction I should work on?
Your warm help is appreciated.
Matplotlib is a library for plotting data. Generally if you're trying to do any computation you'd use a library suited for that.
numpy is a very popular library for doing numerical computation in Python. It just so happens they have a fairly extensive set of fft and ifft methods.
I would check them out here and see if they can solve your problem.
One thing commonly done (for example in the source separation community) is to use the phase data of the original signal (before transformation where applied to it) - the result is much better than null or random phase, and not so far from algorithms aiming at reconstructing the phase information from scratch.
A classic reconstruction algorithm is Griffin&Lim's, described in the paper "Signal estimation from modified short-time Fourier transform". This is an iterative algorithm, each iteration requires a full STFT / inverse STFT, which makes it quite costly.
This problem is indeed an active area of research, a search for STFT + reconstruction + magnitude will yield plenty of papers aiming at improving on Griffin&Lim in terms of signal quality and/or computational efficiency.
You can find detailed dicussion hereThread on DSP Stack Exchange
