Authors
- Sungho Lee*
- Marco A. Martínez-Ramírez
- Wei-Hsiang Liao
- Stefan Uhlich*
- Giorgio Fabbro*
- Kyogu Lee*
- Yuki Mitsufuji
* External authors
Venue
- DAFx-24
Date
- 2024
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SEARCHING FOR MUSIC MIXING GRAPHS: A PRUNING APPROACH
Sungho Lee*
Marco A. Martínez-Ramírez
Stefan Uhlich*
Giorgio Fabbro*
Kyogu Lee*
* External authors
DAFx-24
2024
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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