ddsp
RaveForce
ddsp | RaveForce | |
---|---|---|
3 | 6 | |
2,779 | 196 | |
1.2% | - | |
1.5 | 0.0 | |
13 days ago | almost 2 years ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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ddsp
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Making Synthesized Sounds More Acoustic
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.
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I need to learn audio processing in Python.
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
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Kornia: Differential Computer Vision
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
RaveForce
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AI-generated sad girl with piano performs the text of the MIT License
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.
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Making Synthesized Sounds More Acoustic
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!
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Unloop: A generative music looper that doesn’t repeat itself
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/
- Show HN: Catchy melodies made with a diffusion-based neural net assistant
- RaveForce in 2022: The OpenAI Gym style toolkit for music generation experiments just got better
- Show HN: RaveForce – An OpenAI Gym style toolkit for music generation experiment
What are some alternatives?
awesome-python-scientific-audio - Curated list of python software and packages related to scientific research in audio
keygen - Keygen composes original music in the form of midi files.
MidiTok - MIDI / symbolic music tokenizers for Deep Learning models 🎶
Lars-Ulrich-Challenge - Algorithmic and AI MIDI Drums Generator Implementation
SunflowerOS - AR OS
unloop - a co-creative looper that uses generative modeling to **not** repeat itself.