Lookupthencielingfan

I’m a musician, and a holy grail for digital audio is polyphonic pitch tracking in real time, to output midi for controlling other instruments. I believe this can be easily done using existing FFT with AI and machine learning libraries. Someone already did it themselves using python, but its a hobby effort in python networked to Pure data and there’s some latency. Its just odd that its not widely done already in a cleaner implementation (a stand-alone pure data external for example)

Is there a reason why AI like this isn’t already widely implemented? It seems it would have applications everywhere as it is now. Is most of it just proprietary, or a lack of open source libraries?

Thats great!

I’ve been exploring using an embedded platform as a standalone environment for music performance. With things like Pure data and Jack, its pretty easy to make up any knobby bleepy machine you can imagine on a little ARM board. Seeing things like AI and machine learning is exciting, and I’ve always wondered how they’d apply to music (there’s a lot of algorithmic music composition and FFT things that people do in pure data).

Thank you! I really appreciate this, and I’m excited to look through all of these.

The best one I’ve personally seen for real time use, required that you individually play each note into a machine learning library before hand. It seemed to be pretty accurate, and if you gave it less notes, it would then falsely detect harmonics. That was found to be musically useful, introducing random chance, while still musically consonant. It just had a lot of latency, which could be helped by finding a way to code it more efficiently.

There are a few extremely proprietary examples out there also, but its often more limiting than useful because of the application specific ways they are released, at a premium price. It forces you into specific commercial environments. So everything you mentioned is going to be really helpful.