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.

翻译：鉴于最近在音乐源分离和自动混合方面的进展,消除音乐音效是朝着开发自动混合系统迈出的有意义的一步。本文件的重点是消除音乐制作中吉他音效的扭曲性影响。我们探讨为源分离和音效建模设计的神经网络能否解决消除影响的问题。我们的方法证明对混合处理过的和清洁信号的效果特别有效。这些模型的质量更高,而且比基于稀疏优化的先进解决方案要快得多。我们证明这些模型不仅适合解密,而且适合其他类型的扭曲效应。我们通过讨论结果,强调多种评价指标对于评估扭曲效果清除重建的不同方面是有用的。