论文标题
失真音频效果：学习如何恢复清洁信号
Distortion Audio Effects: Learning How to Recover the Clean Signal
论文作者
论文摘要
鉴于音乐源分离和自动混合的最新进展，删除音乐曲目中的音频效果是开发自动混合系统的有意义的一步。本文着重于消除适用于音乐制作中吉他曲目的失真音频效果。我们探索是否可以通过设计用于源分离和音频效应建模的神经网络来解决效果的去除。 我们的方法证明对混合处理和清洁信号的效果特别有效。与基于稀疏优化的最先进的解决方案相比，这些模型获得了更好的质量和更快的推断。我们证明这些模型不仅适合倾斜，而且适用于其他类型的失真效应。通过讨论结果，我们强调了多个评估指标的有用性，以评估重建的不同方面的失真效果去除。
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.
