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Well the first problem is that nyq = fs/2, not 2*fs.


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As I am a newbie in DSP and I thought this article has a clear explanation, I did not try to answer your questions myself. But you said you have read it and thought these questions are still ambiguous, then I want to try one more time to give my answers. First, about the concepts of bins and bands, they confused me a lot in the beginning when I was trying to ...


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The below tutorial has the clearest explanation I have read about Mel, I think it can answer all of your questions. Mel Frequency Cepstral Coefficient(MFCC) tutorial


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Please check librosa which is really nice python library. It already has a bunch of algorithms implemented for pitch detection. Also instead of FFT try using CQT which reports spectral content of a signal but on a human friendly scale (octaves)


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What you are asking about is typically called "pitch detection". There are 100s of articles written on the topic. A good starter would be this one: https://ccrma.stanford.edu/~pdelac/154/m154paper.htm If you need fairly high precision (for example a tuner application), I recommend a phase locked loop (PLL) or delay locked loop. There is a ...


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You could use cross correlation for this purpose. For an uncorrelated signal $x(t)$, $\textrm{corr}(x(t), x(t + \Delta t)) = c \delta(\Delta t)$, i.e. an delayed impulse. This can be expanded to comprise multiple echoed copies $$ \textrm{corr}\left( \sum_{i=0}^{N} a_i x(t + \Delta t_i), \sum_{j=0}^{N} b_j x(t + \Delta t_j) \right) = c \sum_{i=0}^{N} \sum_{...


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This was my first google hit: https://medium.com/analytics-vidhya/understanding-the-mel-spectrogram-fca2afa2ce53 It seems to explain both how and why? Mel frequency binning is afaik only used when human perception is involved. Either directly, or for signals that have probably been adapted to human hearing (such as human speech). It is a way to represent ...


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