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While deep generative models have empowered music generation, it remains achallenging and under-explored problem to edit an existing musical piece atfine granularity. In this paper, we propose SDMuse, a unified StochasticDifferential Music editing and generation framework, which can not only composea whole musical piece from scratch, but also modify existing musical pieces inmany ways, such as combination, continuation, inpainting, and styletransferring. The proposed SDMuse follows a two-stage pipeline to achieve musicgeneration and editing on top of a hybrid representation including pianorolland MIDI-event. In particular, SDMuse first generates/edits pianoroll byiteratively denoising through a stochastic differential equation (SDE) based ona diffusion model generative prior, and then refines the generated pianorolland predicts MIDI-event tokens auto-regressively. We evaluate the generatedmusic of our method on ailabs1k7 pop music dataset in terms of quality andcontrollability on various music editing and generation tasks. Experimentalresults demonstrate the effectiveness of our proposed stochastic differentialmusic editing and generation process, as well as the hybrid representations.

SDMuse: Stochastic Differential Music Editing and Generation via Hybrid Representation