  Music rearrangement is a common music practice of reconstructing and
reconceptualizing a piece using new composition or instrumentation styles,
which is also an important task of automatic music generation. Existing studies
typically model the mapping from a source piece to a target piece via
supervised learning. In this paper, we tackle rearrangement problems via
self-supervised learning, in which the mapping styles can be regarded as
conditions and controlled in a flexible way. Specifically, we are inspired by
the representation disentanglement idea and propose Q&A, a query-based
algorithm for multi-track music rearrangement under an encoder-decoder
framework. Q&A learns both a content representation from the mixture and
function (style) representations from each individual track, while the latter
queries the former in order to rearrange a new piece. Our current model focuses
on popular music and provides a controllable pathway to four scenarios: 1)
re-instrumentation, 2) piano cover generation, 3) orchestration, and 4) voice
separation. Experiments show that our query system achieves high-quality
rearrangement results with delicate multi-track structures, significantly
outperforming the baselines.
