  Conventional singing voice conversion (SVC) methods often suffer from
operating in high-resolution audio owing to a high dimensionality of data. In
this paper, we propose a hierarchical representation learning that enables the
learning of disentangled representations with multiple resolutions
independently. With the learned disentangled representations, the proposed
method progressively performs SVC from low to high resolutions. Experimental
results show that the proposed method outperforms baselines that operate with a
single resolution in terms of mean opinion score (MOS), similarity score, and
pitch accuracy.
