Music Source Separation in the Waveform Domain

TL;DR

This paper introduces Demucs, a novel waveform-to-waveform music source separation model with a U-Net and LSTM, outperforming existing methods in accuracy and naturalness, and capable of being efficiently compressed.

Contribution

Demucs is a new waveform domain model for music source separation that surpasses state-of-the-art spectrogram-based methods and Conv-Tasnet, with improved naturalness and efficiency.

Findings

Demucs achieves 6.3 SDR on MusDB, surpassing previous methods.

Proper data augmentation enhances Demucs performance.

Demucs can be compressed to 120MB without accuracy loss.

Abstract

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.