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As a lot of DSP tasks, there is of course some people in artificial intelligence working in that, using the ubiquitous so-called Deep Learning. So as posed by @hotpaw2, long gaps are unlikely to be filled with a single video. However, interpolation of missing audio is also called "audio inpainting", and there is a recent paper on using ...


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Thanks again @A_A! Yes, I am using exactly the same function (scipy.signal.coherence). I have a roughly flat coherence over all frequencies (f > 5Hz) which is good: it means that the 2nd device doesn't magnify some specific freqs unnecessarily and have the same similarity with device 1 over all freq ranges. Just as a short recap: my aim is to kind of ...


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That's not true, it's not better. The thing is: the matched filter just implements the projection in the signal vector space, onto the signal vector itself (or a multiple thereof). (You'll find correlation is just an inner product in that space.) The line through that vector is the signal subspace, the plane to which that vector is normal is the noise space. ...


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Yes and no. In principle, you can use the peak of your correlation function. However, it is not at 10000. The correlation function is symmetric around 0, so your peak is actually much closer to zero than you think. This is one of the reasons why xcorr returns two paramters, one for the lags at which the function is calculated. The correct way of plotting the ...


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You can express correlation in the frequency domain but the closest thing to what you are describing would be Coherence. It returns a number between 0 and 1 per each frequency bin of the spectrum. The closest this number is to 1 the more "similar" the signals would be and you can average this number over the whole spectrum or over a frequency range....


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