Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion
CoRR(2024)
摘要
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, please visit our project site:
https://scg-rule-guided-music.github.io/.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要