  Consumer-grade music recordings such as those captured by mobile devices
typically contain distortions in the form of background noise, reverb, and
microphone-induced EQ. This paper presents a deep learning approach to enhance
low-quality music recordings by combining (i) an image-to-image translation
model for manipulating audio in its mel-spectrogram representation and (ii) a
music vocoding model for mapping synthetically generated mel-spectrograms to
perceptually realistic waveforms. We find that this approach to music
enhancement outperforms baselines which use classical methods for
mel-spectrogram inversion and an end-to-end approach directly mapping noisy
waveforms to clean waveforms. Additionally, in evaluating the proposed method
with a listening test, we analyze the reliability of common audio enhancement
evaluation metrics when used in the music domain.
