Symbolic Music Generation with Diffusion Models
Gautam Mittal, Jesse Engel, Curtis Hawthorne, Ian Simon · arXiv (Cornell University) · 2021
Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in a variety of continuous domains. However, due to their Langevin-inspired sampling mechanisms, their application to discrete symbolic music data has been limited. In this work, we present a technique for training diffusion models on symbolic music data by parameterizing the discrete domain in the continuous latent space of a pre-trained variational autoencoder. Our method is non-autoregressive and learns to generate sequences of latent embeddings through the reverse process and offers parallel generation with a constant number of iterative refinement steps. We show strong unconditional generation and post-hoc conditional infilling results compared to autoregressive language models operating over the same continuous embeddings.