Singing voice separation with deep U-Net convolutional networks

Jansson, A., Humphrey, E., Montecchio, N., Bittner, R., Kumar, A. & Weyde, T. (2017). Singing voice separation with deep U-Net convolutional networks. Paper presented at the 18th International Society for Music Information Retrieval Conference, 23-27 Oct 2017, Suzhou, China.

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Abstract

The decomposition of a music audio signal into its vocal and backing track components is analogous to image-to-image translation, where a mixed spectrogram is transformed into its constituent sources. We propose a novel application of the U-Net architecture — initially developed for medical imaging — for the task of source separation, given its proven capacity for recreating the fine, low-level detail required for high-quality audio reproduction. Through both quantitative evaluation and subjective assessment, experiments demonstrate that the proposed algorithm achieves state-of-the-art performance.

Item Type: Conference or Workshop Item (Paper)
Divisions: School of Arts > Department of Creative Practice & Enterprise - Centre for Music Studies
URI: http://openaccess.city.ac.uk/id/eprint/19289

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