  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). (ii) We propose a new scheme to leverage unlabeled music. We train a
first model to extract parts with at least one source silent in unlabeled
tracks, for instance without bass. We remix this extract with a bass line taken
from the supervised dataset to form a new weakly supervised training example.
Combining our architecture and scheme, we show that waveform methods can play
in the same ballpark as spectrogram ones.
