  Music mixing is compositional -- experts combine multiple audio processors to
achieve a cohesive mix from dry source tracks. We propose a method to reverse
engineer this process from the input and output audio. First, we create a
mixing console that applies all available processors to every chain. Then,
after the initial console parameter optimization, we alternate between removing
redundant processors and fine-tuning. We achieve this through differentiable
implementation of both processors and pruning. Consequently, we find a sparse
mixing graph that achieves nearly identical matching quality of the full mixing
console. We apply this procedure to dry-mix pairs from various datasets and
collect graphs that also can be used to train neural networks for music mixing
applications.
