Source separation models either work on the spectrogram or waveform domain.
In this work, we show how to perform end-to-end hybrid source separation,
letting the model decide which domain is best suited for each source, and even
combining both. The proposed hybrid version of the Demucs architecture won the
Music Demixing Challenge 2021 organized by Sony. This architecture also comes
with additional improvements, such as compressed residual branches, local
attention or singular value regularization. Overall, a 1.4 dB improvement of
the Signal-To-Distortion (SDR) was observed across all sources as measured on
the MusDB HQ dataset, an improvement confirmed by human subjective evaluation,
with an overall quality rated at 2.83 out of 5 (2.36 for the non hybrid
Demucs), and absence of contamination at 3.04 (against 2.37 for the non hybrid
Demucs and 2.44 for the second ranking model submitted at the competition).

本文介绍了在音频信号源分离中采用端到端混合模型的方法，该方法使模型决定哪个领域最适合每个源，并将两者结合起来。该方法在 Sony 举办的 2021 年音乐分离挑战中获胜。实验结果表明，该模型在 MusDB HQ 数据集上实现了 1.4dB 的 SDR 改进，并得到了人类主观评价的认可。