Earlier quoted context omitted.
In my first post, quite a lot of alternatives were discussed: https://news.ycombinator.com/item?id=36707877 The model I'm using is called Open-Unmix ( https://github.com/sigsep/open-unmix-pytorch ). In 2021, there was an update to Open-Unmix to include new weights, UMX-L, which made it perform better than it used to on the older weights (UMXHQ). In the grand landscape of music demixing, I don't think UMX-L is near th…
It's super exciting to see people using open-unmix like this! I worked with the creator of the model to try to do the same as a university project! Our solution was... not great, to say the least, but I'm happy someone else managed to do it!
We have since discussed my projects a couple of times! I even got the idea for a streaming LSTM from him.
I think music demixing in general owes a lot of thanks to Open-Unmix and co (https://github.com/sigsep), who have relentlessly been publishing open-source models and related code (source separation metrics, dataset loaders, etc.) for years, and who blew the industry open with their MDX 21 [1] and SDX 23 [2] AI challenges.
[1]: https://www.aicrowd.com/challenges/music-demixing-challenge-...
[2]: https://www.aicrowd.com/challenges/sound-demixing-challenge-...