Singing Voice Separation with Deep U-Net Convolutional Networks

The decomposition of a music audio signal into its vocal and backing track components is analogous to image-toimage 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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Speech Separation iKala U-Net NSDR 11.094 (Vocal); 14.435 (Instrumental) # 1