失真恢复：一种用于吉他效果移除的两阶段方法
Distortion Recovery: A Two-Stage Method for Guitar Effect Removal
AI总结：
针对电吉他录音失真移除问题，提出使用商业级VST插件数据集的两阶段方法，通过Mel频谱图处理与神经声码器生成纯净声音，主客观指标均证明其优于现有方法。
摘要：
从电吉他录音中移除音频效果使得后期制作和声音编辑更加容易。音频失真恢复模型不仅提高了吉他声音的清晰度，还为混音和母带制作中的创意调整开辟了新机遇。尽管在创建此类模型方面已取得进展，但以往的努力主要集中在合成失真上，这些失真可能过于简单，无法准确捕捉真实录音中出现的复杂性。在本文中，我们通过使用由商业级音频效果VST插件渲染的吉他录音数据集来处理该任务。此外，我们引入了一种用于音频失真恢复的新型两阶段方法。其思想是首先在第一阶段于Mel频谱图域处理音频信号，然后在第二阶段使用神经声码器从处理后的Mel频谱图生成纯净的原始吉他声音。我们报告了一组实验，通过主观和客观评估指标证明了我们的方法优于现有方法的有效性。
英文摘要：
Removing audio effects from electric guitar recordings makes it easier for post-production and sound editing. An audio distortion recovery model not only improves the clarity of the guitar sounds but also opens up new opportunities for creative adjustments in mixing and mastering. While progress have been made in creating such models, previous efforts have largely focused on synthetic distortions that may be too simplistic to accurately capture the complexities seen in real-world recordings. In this paper, we tackle the task by using a dataset of guitar recordings rendered with commercial-grade audio effect VST plugins. Moreover, we introduce a novel two-stage methodology for audio distortion recovery. The idea is to firstly process the audio signal in the Mel-spectrogram domain in the first stage, and then use a neural vocoder to generate the pristine original guitar sound from the processed Mel-spectrogram in the second stage. We report a set of experiments demonstrating the effectiveness of our approach over existing methods, through both subjective and objective evaluation metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
