I'm using Snack Library. This library, create the spectrogram as a canvas object but I. should use the spectrogram as a numerical object (every 10 ms I should extract the vector of frequencies). This is the code of Snack Library: c = tkSnack.SnackCanvas (root, height=400) c.pack () c.create_waveform (0, 0, sound=mysound, height=100, zerolevel=1. To compare the different spectrogram preprocessing frontends described above, we trained and tested a different model using each type of preprocessing. We describe the e xperimental setup in the previous post using our “best practices” spectrogram creation parameters. Models were trained on a 52 species dataset. archive.org. Currently, I'm writing a Python script, which should do the following: read an audio file respectively a wav file via scipy.io.wavfile.read(). calculate.

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