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 first show that an adaptation of Conv-Tasnet (Luo & Mesgarani, 2019), a waveform-to-waveform model for source separation for speech, significantly beats the state-of-the-art on the MusDB dataset, the standard benchmark of multi-instrument source separation. Second, we observe that Conv-Tasnet follows a masking approach on the input signal, which has the potential drawback of removing parts of the relevant source without the capacity to reconstruct it. We propose Demucs, a new waveform-towaveform model, which has an architecture closer to models for audio generation with more capacity on the decoder. Experiments on the MusDB dataset show that Demucs beats previously reported results in terms of signal to distortion ratio (SDR), but lower than Conv-Tasnet. Human evaluations show that Demucs has significantly higher quality (as assessed by mean opinion score) than Conv-Tasnet, but slightly more contamination from other sources, which explains the difference in SDR. Additional experiments with a larger dataset suggest that the gap in SDR between Demucs and Conv-Tasnet shrinks, showing that our approach is promising.
In this work, the authors consider the task of supervised music source separation, i.e., separating out the components (bass, drums, voice, other) out of a mixed music track. In particular, the work considers the task of supervised source separation where the individual target tracks are available during training time. The main contribution of this work is the improvement of an end-to-end waveform-to-waveform separation models through a number of architectural changes that allow such waveform-to-waveform models to perform comparably with other current state-of-the-art methods that instead operate in the spectrogram domain.
Overall, the paper is generally well written and the method is easy to follow. However, I have a couple of concerns about this work, based on which I would rate the work as “weak reject”:
1. The specific architecture proposed in this task does appear to improve performance for this particular task, but it is not clear to me that the conclusions drawn based on the study in this work will be generally applicable to other related tasks. Also, the final model is still only comparable to the spectrogram-based methods overall, and appears to do a slightly worse job in separating the ‘vocal’ and ‘other’ tracks than these baseline methods. As such, I’m unsure about the impact of this work.
2. The section describing the evaluation metric SDR wasn’t very clear to me. Perhaps it would be better to just refer back to (Vincent et al., 06) and just describe what SDR captures instead? Or alternatively, more details could be added to explain the computation more clearly.
3. Section 4.2: The authors mention that they multiply each source by +/- 1. I didn’t follow why this is being done. Could the authors please clarify.
Minor comments:
1. The notation in Equation 1 is slightly confusing because ‘s’ is used to index both the mixture and the sources. I think x \in \mathcal{D} would be clearer. Similarly I wonder if x_s \in \mathbb{R}^C \times \mathbb{R}^T would be clearer than x_s \in \mathbb{R}^{C,T}
2. The reference (Oord et al., 2017) should be rendered as (van den Oord et al., 2017) 
3. The Figure uses K, and S to denote Kernel Width and stride, but this isn’t explicitly mentioned in the text. Perhaps it would be useful, in Section 3.1 to write: “... convolution with kernel width, K=8, and stride, S=4, ...” or something similar, and updating the caption to explain the notation.
4. Section 3.2: “The decoder is almost the symmetric of the encoder” --> “The decoder is almost the inverse of the encoder” would perhaps be better?
5. Section 3.2: “The final layer … S.C_0 ...” --> “The final layer … S * C_0 ...”
6. The notation in Equation 1 and Equation 2 is slightly inconsistent (lower-case vs. upper-case L for the loss function)
7. Section 4.3: “additionnaly” --> “additionally”; “32 GB or RAM” --> “32 GB of RAM”