  Deep generative models are now able to synthesize high-quality audio signals,
shifting the critical aspect in their development from audio quality to control
capabilities. Although text-to-music generation is getting largely adopted by
the general public, explicit control and example-based style transfer are more
adequate modalities to capture the intents of artists and musicians.
  In this paper, we aim to unify explicit control and style transfer within a
single model by separating local and global information to capture musical
structure and timbre respectively. To do so, we leverage the capabilities of
diffusion autoencoders to extract semantic features, in order to build two
representation spaces. We enforce disentanglement between those spaces using an
adversarial criterion and a two-stage training strategy. Our resulting model
can generate audio matching a timbre target, while specifying structure either
with explicit controls or through another audio example. We evaluate our model
on one-shot timbre transfer and MIDI-to-audio tasks on instrumental recordings
and show that we outperform existing baselines in terms of audio quality and
target fidelity. Furthermore, we show that our method can generate cover
versions of complete musical pieces by transferring rhythmic and melodic
content to the style of a target audio in a different genre.
