SDMuse

While deep generative models have empowered music generation, it remains a challenging and under-explored problem to edit an existing musical piece at fine granularity. In this paper, we propose SDMuse, a unified stochastic differential music editing and generation framework, which can not only compose a whole musical piece from scratch, but also modify existing musical pieces in many ways, such as combination, continuation, inpainting, and style transferring. SDMuse is based on a diffusion model generative prior, synthesizing a musical piece by iteratively denoising through a stochastic differential equation.