Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
<|reference_start|>Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion: 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 Stochastic Control Guidance (SCG), 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/.<|reference_end|>
@article{huang2024symbolic,
    title={Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion},
    author={Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang,
  Chandramouli S Sastry, Siddharth Gururani, Sageev Oore, Yisong Yue},
    journal={arXiv preprint arXiv:2402.14285},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14285},
    primaryClass={cs.SD cs.LG eess.AS}
}