DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion

Singing voice conversion (SVC) is one promising technique that can enrich the way of human-computer interaction by en-dowing a computer the ability to produce high-fidelity and expressive singing voice. In this paper, we propose DiffSVC, an SVC system based on denoising diffusion probabilistic model. DiffSVC uses phonetic posteriorgrams (PPGs) as con-tent features. A denoising module is trained in DiffSVC, which takes destroyed mel spectrogram produced by the dif-fusion/forward process and its corresponding step information as input to predict the added Gaussian noise. We use PPGs, fundamental frequency features and loudness features as auxiliary inputs to assist the denoising process. Experi-ments show that DiffSVC can achieve superior conversion performance in terms of naturalness and voice similarity to current state-of-the-art SVC approaches.