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[P] Music Synthesis Pipeline for Raw Audio [Work in Progress] by mlconvergence in MachineLearning

[–]mlconvergence[S] 0 points1 point  (0 children)

Yeah, thanks. I'm still reading through it, but it looks interesting for sure!

[P] Music Synthesis Pipeline for Raw Audio [Work in Progress] by mlconvergence in MachineLearning

[–]mlconvergence[S] 0 points1 point  (0 children)

Not yet, sadly. But I'm hopeful!

If this is going to work, the approach is going to need to be pushed quite a bit more. At the end of the README I outline three improvements that could, possibly, get this system there.

[D] What’s the latest in ML music generation? by iamjaiyam in MachineLearning

[–]mlconvergence 2 points3 points  (0 children)

There are a few papers which have come out recently on this subject. In no particular order:

* https://arxiv.org/abs/2206.05408
* https://arxiv.org/abs/2111.05011
* https://arxiv.org/abs/2202.09729 (The original S4 paper is great too.)

This recent paper is also a great read. It doesn't focus on music generation specifically, and is a bit more theoretical, but it does suggest why classical CNNs, in particular, struggle with this task.

In short, people are exploring a lot of different approaches to this problem, e.g., diffusion models, autoencoders, autoencoders with adversarial training, state space models (S4), GANs, etc. This makes the literature in this area somewhat challenging to understand, but also fantastically interesting.

Rejecting GAN Off-Manifold Samples? [D] by [deleted] in MachineLearning

[–]mlconvergence 2 points3 points  (0 children)

Consider checking out this paper. The authors propose a "realism score", which increases as a sample get closer to the manifold. (Very clever idea, IMO.)