13 ms·

Thanks, yours is very nice too :) For what it's worth, the auto-correlation route is simpler and more reliable. FFT alone is not enough, because the spectrum t

Thanks, yours is very nice too :) For what it's worth, the auto-correlation route is simpler and more reliable. FFT alone is not enough, because the spectrum tends to be rather ambiguous. There's still a pattern to it for every note played, so it should be possible to deduce a note with a neural net. But I just didn't have time for that, maybe later. PS. Give my thingy a try if you have a moment. I'm curious if it actually works with any pianos other than mine.

jacquesm 6y ago
Yes, I know about the auto correlation trick, especially on the low end of the spectrum. I wrote an MP3->midi convertor that is very fast and does not use the GPU or any kind of deep learning, it also gives results that a year ago were SOTA but in the meantime have been passed by deep learning based techniques. I will definitely use your software. What would be great is to have a 'virtual midi device'. If it works well enough are you open to licensing it? We will sooner or later hit this problem and such a component would make our lives a lot easier, it would also allow me to practice on my real piano using the software, which if I didn't want to play the piano I'd give my left arm for. On another note: when you know what should be played it is actually easier: you only need to output a '1' or a '0', what is played is what is expected vs what is played is not what is expected. This may be a simpler problem to solve, and in that context it might even be able to detect chords.
- apankrat 6y ago
  Re: a virtual midi device - I don't know the specifics of WebMIDI, but I'd think it should be rather trivial to code on top of any pitch detector, including mine. In terms of licensing - the code is already BSD-2. Re: the another note - yes, indeed. I too realized this, because mis-detected notes are usually off by a full octave. As a side note, after going reading through various papers on the subject, it would seem that an ultimate note detector should really be based on a Const-Q spectrum analyzer feeding into an NN. Training the net, even from scratch, can be as simple as sitting in front of a piano and then repeatedly playing each note in all possible ways. Then, throw this detector in a group with waveform-based detectors, get a weighted consensus and that should, in theory, be pretty damn accurate.
  - jacquesm 6y ago
    I've done that, and this is roughly how magenta works, which is pretty good, on random input it has about 92.5% accuracy which I think is very impressive.

jacquesm 6y ago

Yes, I know about the auto correlation trick, especially on the low end of the spectrum. I wrote an MP3->midi convertor that is very fast and does not use the GPU or any kind of deep learning, it also gives results that a year ago were SOTA but in the meantime have been passed by deep learning based techniques. I will definitely use your software. What would be great is to have a 'virtual midi device'. If it works well enough are you open to licensing it? We will sooner or later hit this problem and such a component would make our lives a lot easier, it would also allow me to practice on my real piano using the software, which if I didn't want to play the piano I'd give my left arm for. On another note: when you know what should be played it is actually easier: you only need to output a '1' or a '0', what is played is what is expected vs what is played is not what is expected. This may be a simpler problem to solve, and in that context it might even be able to detect chords.

apankrat 6y ago
Re: a virtual midi device - I don't know the specifics of WebMIDI, but I'd think it should be rather trivial to code on top of any pitch detector, including mine. In terms of licensing - the code is already BSD-2. Re: the another note - yes, indeed. I too realized this, because mis-detected notes are usually off by a full octave. As a side note, after going reading through various papers on the subject, it would seem that an ultimate note detector should really be based on a Const-Q spectrum analyzer feeding into an NN. Training the net, even from scratch, can be as simple as sitting in front of a piano and then repeatedly playing each note in all possible ways. Then, throw this detector in a group with waveform-based detectors, get a weighted consensus and that should, in theory, be pretty damn accurate.
- jacquesm 6y ago
  I've done that, and this is roughly how magenta works, which is pretty good, on random input it has about 92.5% accuracy which I think is very impressive.