  Singing voice conversion (SVC) is one promising technique which can enrich
the way of human-computer interaction by endowing 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 content features. A denoising
module is trained in DiffSVC, which takes destroyed mel spectrogram produced by
the diffusion/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 input to assist the denoising
process. Experiments show that DiffSVC can achieve superior conversion
performance in terms of naturalness and voice similarity to current
state-of-the-art SVC approaches.
