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Yes, it's extremely doable. Although NNs are excellent at this kind of classification training, they may not even be necessary -- with a well-chosen set of features, just the classic clustering algorithms such as a Gaussian mixture model, or principal component analysis, would probably do as well. Modern libraries can get this stuff right about 95% of the ...


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Typically, for analyses like this, you do overlapping windows with a hop size that is fixed fraction of the window size. So, for example, you might start by doing analyses of 1024 frames per window, and move the window forward 256 samples per analysis. Depending on how the data looks then, you may or may not want to apply a window function (like Hamming or ...


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Some ideas: You could train the model using audio with randomly chosen speaker--mic system impulse responses applied. To get you started, there are microphone impulse response (IR) packs available, intended for music production purposes. Or you could perhaps generate synthetic impulse responses that cover the variability in real-world impulse responses. Or ...


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