Distortion Recovery: A Two-Stage Method for Guitar Effect Removal

TL;DR

This paper presents a novel two-stage method for removing guitar effects from recordings, utilizing Mel-spectrogram processing and neural vocoders, to improve sound clarity and enable creative mixing adjustments.

Contribution

Introduces a new two-stage approach using Mel-spectrograms and neural vocoders for more accurate distortion recovery in real-world guitar recordings.

Findings

Outperforms existing methods in subjective evaluations

Achieves higher objective metrics in distortion removal

Enhances guitar sound clarity post-processing

Abstract

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.