Controllable Music Production with Diffusion Models and Guidance Gradients

This paper was accepted at the Diffusion Models workshop at NeurIPS 2023.

We demonstrate how conditional generation from diffusion models can be used to tackle a variety of realistic tasks in the production of music in 44.1kHz stereo audio with sampling-time guidance. The scenarios we consider include continuation, inpainting and regeneration of musical audio, the creation of smooth transitions between two different music tracks, and the transfer of desired stylistic characteristics to existing audio clips. We achieve this by applying guidance at sampling time in a simple framework that supports both reconstruction and classification losses, or any combination of the two. This approach ensures that generated audio can match its surrounding context, or conform to a class distribution or latent representation specified relative to any suitable pre-trained classifier or embedding model.

We show randomly chosen samples for a number of creative applications in Table 1, each conditioned on a given audio prompt. For each task and prompt we show samples from the different models described in the paper.

Task types:

• infill: replace the middle two seconds of the prompt
• regeneration: regenerate the middle two seconds of the prompt
• continuation: generate a new continuation starting from the first 2.4s of the prompt
• transitions: regenerate a crossfaded section between two tracks
• guidance: generate a new clip conditioned on the PaSST classifier embedding of the prompt

Prompts are drawn from a test split of the Free Music Archive dataset, published by Michaël Defferrard et al. under a Creative Commons Attribution 4.0 International License (CC BY 4.0).