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We study the problem of source separation for music using deep learning withfour known sources: drums, bass, vocals and other accompaniments.State-of-the-art approaches predict soft masks over mixture spectrograms whilemethods working on the waveform are lagging behind as measured on the standardMusDB benchmark. Our contribution is two fold. (i) We introduce a simpleconvolutional and recurrent model that outperforms the state-of-the-art modelon waveforms, that is, Wave-U-Net, by 1.6 points of SDR (signal to distortionratio). (ii) We propose a new scheme to leverage unlabeled music. We train afirst model to extract parts with at least one source silent in unlabeledtracks, for instance without bass. We remix this extract with a bass line takenfrom the supervised dataset to form a new weakly supervised training example.Combining our architecture and scheme, we show that waveform methods can playin the same ballpark as spectrogram ones.

Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed