Searching For Music Mixing Graphs: A Pruning Approach
Sungho Lee, Marco A. Mart\'inez-Ram\'irez, Wei-Hsiang Liao, Stefan, Uhlich, Giorgio Fabbro, Kyogu Lee, and Yuki Mitsufuji

TL;DR
This paper introduces a differentiable pruning method to reverse engineer and simplify music mixing graphs, resulting in sparse models that match the quality of full mixing consoles and can aid neural network training.
Contribution
It presents a novel differentiable pruning approach to extract sparse, effective music mixing graphs from complex processing chains.
Findings
Sparse mixing graphs match full console quality
Method generalizes across various datasets
Graphs can be used for neural network training
Abstract
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.
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Digital Humanities and Scholarship
