  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.
