P2-09: Distortion Audio Effects: Learning How to Recover the Clean Signal
Imort, Johannes, Fabbro, Giorgio*, Martinez Ramirez, Marco A, Uhlich, Stefan, Koyama, Yuichiro, Mitsufuji, Yuki
Subjects (starting with primary): MIR fundamentals and methodology -> music signal processing ; MIR tasks -> music synthesis and transformation ; Applications -> performance, and production ; Domain knowledge -> machine learning/artificial intelligence for music ; MIR tasks -> sound source separation
Presented In-person, in Bengaluru: 4-minute short-format presentation
Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing system. This paper focuses on removing distortion audio effects applied to guitar tracks in music production. We explore whether effect removal can be solved by neural networks designed for source separation and audio effect modeling.
Our approach proves particularly effective for effects that mix the processed and clean signals. The models achieve better quality and significantly faster inference compared to state-of-the-art solutions based on sparse optimization. We demonstrate that the models are suitable not only for declipping but also for other types of distortion effects. By discussing the results, we stress the usefulness of multiple evaluation metrics to assess different aspects of reconstruction in distortion effect removal.