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

Understand

We study the problem of source separation for music using deep learning with four known sources: drums, bass, vocals and other accompaniments.

• State-of-the-art approaches predict soft masks over mixture spectrograms while methods working on the waveform are lagging behind as measured on the standard MusDB benchmark.
• Our contribution is two fold.
• (i) We introduce a simple convolutional and recurrent model that outperforms the state-of-the-art model on waveforms, that is, Wave-U-Net, by 1.6 points of SDR (signal to distortion ratio).