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Given the recent advances in music source separation and automatic mixing,removing audio effects in music tracks is a meaningful step toward developingan automated remixing system. This paper focuses on removing distortion audioeffects applied to guitar tracks in music production. We explore whether effectremoval can be solved by neural networks designed for source separation andaudio effect modeling.  Our approach proves particularly effective for effects that mix the processedand clean signals. The models achieve better quality and significantly fasterinference compared to state-of-the-art solutions based on sparse optimization.We demonstrate that the models are suitable not only for declipping but alsofor other types of distortion effects. By discussing the results, we stress theusefulness of multiple evaluation metrics to assess different aspects ofreconstruction in distortion effect removal.

Distortion Audio Effects: Learning How to Recover the Clean Signal