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See the section "On Pitch Estimation" which addresses exactly that: --- Pitch detection (also called fundamental frequency estimation) is not an exact science.
by zeroxfe 7y ago
See the section "On Pitch Estimation" which addresses exactly that:
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Pitch detection (also called fundamental frequency estimation) is not an exact science. What your brain perceives as pitch is a function of lots of different variables, from the physical materials that generate the sounds to your body's physiological structure.
One would presume that you can simply transform a signal to its frequency domain representation, and look at the peak frequencies. This would work for a sine wave, but as soon as you introduce any kind of timbre (e.g., when you sing, or play a note on a guitar), the spectrum is flooded with overtones and harmonic partials.
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All this said, you don't need deep learning for decent pitch detection -- it's a solved problem, and there are lots of well-known algorithms for it. Deep learning is useful for more advanced music info retrieval such as interval and chord recognition, which was one of my goals with this experiment.
- AstralStorm 7y agoYou can use deep networks to get better resolution there though, keyword is spectral reassignment, normal methods use maximum likelihood. What you will get is a variant spectrogram. Method is related to edge directed interpolation and bilateral transform. (You can do the same with log-cepstrum.) I find this whole exercise linked an undergrad level toy, even in such basic thing as pitch. The real problem is polyphony and instrument segmentation which this does not begin to touch.