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Applications of deep learning to automatic multitrack mixing are largelyunexplored. This is partly due to the limited available data, coupled with thefact that such data is relatively unstructured and variable. To address thesechallenges, we propose a domain-inspired model with a strong inductive bias forthe mixing task. We achieve this with the application of pre-trainedsub-networks and weight sharing, as well as with a sum/difference stereo lossfunction. The proposed model can be trained with a limited number of examples,is permutation invariant with respect to the input ordering, and places nolimit on the number of input sources. Furthermore, it produces human-readablemixing parameters, allowing users to manually adjust or refine the generatedmix. Results from a perceptual evaluation involving audio engineers indicatethat our approach generates mixes that outperform baseline approaches. To thebest of our knowledge, this work demonstrates the first approach in learningmultitrack mixing conventions from real-world data at the waveform level,without knowledge of the underlying mixing parameters.

Automatic multitrack mixing with a differentiable mixing console of neural audio effects