  Singing voice conversion (SVC) aims to convert the voice of one singer to
that of other singers while keeping the singing content and melody. On top of
recent voice conversion works, we propose a novel model to steadily convert
songs while keeping their naturalness and intonation. We build an end-to-end
architecture, taking phonetic posteriorgrams (PPGs) as inputs and generating
mel spectrograms. Specifically, we implement two separate encoders: one encodes
PPGs as content, and the other compresses mel spectrograms to supply acoustic
and musical information. To improve the performance on timbre and melody, an
adversarial singer confusion module and a mel-regressive representation
learning module are designed for the model. Objective and subjective
experiments are conducted on our private Chinese singing corpus. Comparing with
the baselines, our methods can significantly improve the conversion performance
in terms of naturalness, melody, and voice similarity. Moreover, our PPG-based
method is proved to be robust for noisy sources.
