1. Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
- Author
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Huang, Yujia, Ghatare, Adishree, Liu, Yuanzhe, Hu, Ziniu, Zhang, Qinsheng, Sastry, Chandramouli S, Gururani, Siddharth, Oore, Sageev, and Yue, Yisong
- Subjects
Computer Science - Sound ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
We study the problem of symbolic music generation (e.g., generating piano rolls), with a technical focus on non-differentiable rule guidance. Musical rules are often expressed in symbolic form on note characteristics, such as note density or chord progression, many of which are non-differentiable which pose a challenge when using them for guided diffusion. We propose \oursfull (\ours), a novel guidance method that only requires forward evaluation of rule functions that can work with pre-trained diffusion models in a plug-and-play way, thus achieving training-free guidance for non-differentiable rules for the first time. Additionally, we introduce a latent diffusion architecture for symbolic music generation with high time resolution, which can be composed with SCG in a plug-and-play fashion. Compared to standard strong baselines in symbolic music generation, this framework demonstrates marked advancements in music quality and rule-based controllability, outperforming current state-of-the-art generators in a variety of settings. For detailed demonstrations, code and model checkpoints, please visit our project website: https://scg-rule-guided-music.github.io/., Comment: ICML 2024 (Oral)
- Published
- 2024