Generating symbolic music using diffusion models
This work addresses music generation for creative applications, but it is incremental as it applies an existing diffusion framework to a new domain with specific adaptations.
The authors tackled symbolic music generation by proposing a diffusion model with a binomial prior for piano rolls, achieving coherent music generation up to the length of training segments and enabling tasks like harmonization and completion.
Denoising Diffusion Probabilistic models have emerged as simple yet very powerful generative models. Unlike other generative models, diffusion models do not suffer from mode collapse or require a discriminator to generate high-quality samples. In this paper, a diffusion model that uses a binomial prior distribution to generate piano rolls is proposed. The paper also proposes an efficient method to train the model and generate samples. The generated music has coherence at time scales up to the length of the training piano roll segments. The paper demonstrates how this model is conditioned on the input and can be used to harmonize a given melody, complete an incomplete piano roll, or generate a variation of a given piece. The code is publicly shared to encourage the use and development of the method by the community.