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Sound source separation has attracted attention from Music InformationRetrieval(MIR) researchers, since it is related to many MIR tasks such asautomatic lyric transcription, singer identification, and voice conversion. Inthis paper, we propose an intuitive spectrogram-based model for sourceseparation by adapting U-Net. We call it Spectrogram-Channels U-Net, whichmeans each channel of the output corresponds to the spectrogram of separatedsource itself. The proposed model can be used for not only singing voiceseparation but also multi-instrument separation by changing only the number ofoutput channels. In addition, we propose a loss function that balances volumesbetween different sources. Finally, we yield performance that isstate-of-the-art on both separation tasks.

Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source