Searching For Music Mixing Graphs: A Pruning Approach
<|reference_start|>Searching For Music Mixing Graphs: A Pruning Approach: 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.<|reference_end|>
@article{lee2024searching,
    title={Searching For Music Mixing Graphs: A Pruning Approach},
    author={Sungho Lee, Marco A. Mart'inez-Ram'irez, Wei-Hsiang Liao, Stefan
  Uhlich, Giorgio Fabbro, Kyogu Lee, and Yuki Mitsufuji},
    journal={arXiv preprint arXiv:2406.01049},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01049},
    primaryClass={cs.SD}
}