  Mixing style transfer automates the generation of a multitrack mix for a
given set of tracks by inferring production attributes from a reference song.
However, existing systems for mixing style transfer are limited in that they
often operate only on a fixed number of tracks, introduce artifacts, and
produce mixes in an end-to-end fashion, without grounding in traditional audio
effects, prohibiting interpretability and controllability. To overcome these
challenges, we introduce Diff-MST, a framework comprising a differentiable
mixing console, a transformer controller, and an audio production style loss
function. By inputting raw tracks and a reference song, our model estimates
control parameters for audio effects within a differentiable mixing console,
producing high-quality mixes and enabling post-hoc adjustments. Moreover, our
architecture supports an arbitrary number of input tracks without source
labelling, enabling real-world applications. We evaluate our model's
performance against robust baselines and showcase the effectiveness of our
approach, architectural design, tailored audio production style loss, and
innovative training methodology for the given task.
