  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.

Audio and Speech Processing;Machine Learning;Sound