RaveForce VS ddsp

Great resources! Thank you for making this.

I'm attaching here a DRL framework I made for music generation, similar to OpenAI Gym. If anyone wants to test the algorithms OP includes, you are welcome to use it. Issues and PRs are also welcome.

https://github.com/chaosprint/RaveForce

Suno is great and I already shared its potential back in v2. I have always believed that the essence of digital music is "organized numbers". I think what needs to be thought about is how to use AI in this process. If you look at the results (numbers) generated, then we are indeed very close. But there is another future I believe: I hope AI can compose music with me, like copilot. This is why I keep working on

https://glicol.org/

and the destination is:

https://github.com/chaosprint/RaveForce

Also want to hear your feedback.

I have a project that aims to do the similar task but with a different approach:

https://github.com/chaosprint/RaveForce

not finished yet, so pr is welcomed!

interesting project! I also have a project for loop generation using DRL:

https://github.com/chaosprint/RaveForce

Haven't kept it up to date for a while as too many things are on my list. But if some of you find it interesting we can chat.

Also if you are interested in generative music, you can't miss: https://www.riffusion.com/

You can actually use thousands of oscillators, which is what Magenta did with their differentiable DSP (DDSP) approach to modeling acoustic instruments including singing:

https://magenta.tensorflow.org/ddsp-vst

https://magenta.tensorflow.org/ddsp

I doesn't sound realistic but it is expressive in an unusual way.

Have you checked out DDSP from magenta? I’m not sure what kind of learning you are trying to implement but they provide a lot of standard signal processing components that are differentiable so you can use them as part of a neural net. There are a handful of collabs that serve as interactive tutorials and the documentation is not bad, I did my bachelors thesis on the topic so I got quite stuck into this for a while

In case you're asking about the parent comment (s/Differential/Differentiable), it means the title should be "differentiable CV" (instead of "differential CV").

"Differentiable" describes some computation for which derivatives can be computed. "Differential" is a more general term which means that something has to do with differences, e.g. differential equations deal with equations that specify how things change together.

With the recent surge in deep learning came significant improvements in optimization techniques and hardware, making it feasible to formulate some computations in a differentiable manner. Doing that allows one to optimize the computation process relatively efficiently, at least in theory. Some other examples: differentiable programming[0] (other differentiable techniques are a subset of this), differentiable rendering[1], differentiable signal processing[2].

[0] https://en.wikipedia.org/wiki/Differentiable_programming

[1] https://arxiv.org/abs/2006.12057

[2] https://github.com/magenta/ddsp