Reverse Engineering of Music Mixing Graphs With Differentiable Processors and Iterative Pruning

Reverse engineering of music mixes aims to uncover how dry source signals are processed and combined to produce a final mix. In this paper, prior works are extended to reflect the compositional nature of mixing and search for a graph of audio processors. First, a mixing console is constructed, applying all available processors to every track and subgroup. With differentiable processor implementations, their parameters are optimized with gradient descent. Next, the process of removing negligible processors and fine-tuning the remaining ones is repeated. This way, the quality of the full mixing console can be preserved while removing approximately two-thirds of the processors. The proposed method can be used not only to analyze individual music mixes but also to collect large-scale graph data for downstream tasks such as automatic mixing. Especially for the latter purpose, efficient implementation of the search is crucial. To this end, an efficient batch-processing method that computes multiple processors in parallel is presented. Also, the “dry/wet” parameter of each processor is exploited to accelerate the search. Extensive quantitative and qualitative analyses are conducted to evaluate the proposed method’s performance, behavior, and computational cost.