  Singing voice conversion aims to convert singer's voice from source to target
without changing singing content. Parallel training data is typically required
for the training of singing voice conversion system, that is however not
practical in real-life applications. Recent encoder-decoder structures, such as
variational autoencoding Wasserstein generative adversarial network (VAW-GAN),
provide an effective way to learn a mapping through non-parallel training data.
In this paper, we propose a singing voice conversion framework that is based on
VAW-GAN. We train an encoder to disentangle singer identity and singing prosody
(F0 contour) from phonetic content. By conditioning on singer identity and F0,
the decoder generates output spectral features with unseen target singer
identity, and improves the F0 rendering. Experimental results show that the
proposed framework achieves better performance than the baseline frameworks.
