  Source separation for music is the task of isolating contributions, or stems,
from different instruments recorded individually and arranged together to form
a song. Such components include voice, bass, drums and any other
accompaniments.Contrarily to many audio synthesis tasks where the best
performances are achieved by models that directly generate the waveform, the
state-of-the-art in source separation for music is to compute masks on the
magnitude spectrum. In this paper, we compare two waveform domain
architectures. We first adapt Conv-Tasnet, initially developed for speech
source separation,to the task of music source separation. While Conv-Tasnet
beats many existing spectrogram-domain methods, it suffersfrom significant
artifacts, as shown by human evaluations. We propose instead Demucs, a novel
waveform-to-waveform model,with a U-Net structure and bidirectional
LSTM.Experiments on the MusDB dataset show that, with proper data augmentation,
Demucs beats allexisting state-of-the-art architectures, including Conv-Tasnet,
with 6.3 SDR on average, (and up to 6.8 with 150 extra training songs, even
surpassing the IRM oracle for the bass source).Using recent development in
model quantization, Demucs can be compressed down to 120MBwithout any loss of
accuracy.We also provide human evaluations, showing that Demucs benefit from a
large advantagein terms of the naturalness of the audio. However, it suffers
from some bleeding,especially between the vocals and other source.
