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Consumer-grade music recordings such as those captured by mobile devicestypically contain distortions in the form of background noise, reverb, andmicrophone-induced EQ. This paper presents a deep learning approach to enhancelow-quality music recordings by combining (i) an image-to-image translationmodel for manipulating audio in its mel-spectrogram representation and (ii) amusic vocoding model for mapping synthetically generated mel-spectrograms toperceptually realistic waveforms. We find that this approach to musicenhancement outperforms baselines which use classical methods formel-spectrogram inversion and an end-to-end approach directly mapping noisywaveforms to clean waveforms. Additionally, in evaluating the proposed methodwith a listening test, we analyze the reliability of common audio enhancementevaluation metrics when used in the music domain.

Music Enhancement via Image Translation and Vocoding