  Applications of deep learning to automatic multitrack mixing are largely
unexplored. This is partly due to the limited available data, coupled with the
fact that such data is relatively unstructured and variable. To address these
challenges, we propose a domain-inspired model with a strong inductive bias for
the mixing task. We achieve this with the application of pre-trained
sub-networks and weight sharing, as well as with a sum/difference stereo loss
function. The proposed model can be trained with a limited number of examples,
is permutation invariant with respect to the input ordering, and places no
limit on the number of input sources. Furthermore, it produces human-readable
mixing parameters, allowing users to manually adjust or refine the generated
mix. Results from a perceptual evaluation involving audio engineers indicate
that our approach generates mixes that outperform baseline approaches. To the
best of our knowledge, this work demonstrates the first approach in learning
multitrack mixing conventions from real-world data at the waveform level,
without knowledge of the underlying mixing parameters.
