CatNet: music source separation system with mix-audio augmentation

Understand

Music source separation (MSS) is the task of separating a music piece into individual sources, such as vocals and accompaniment.

• Recently, neural network based methods have been applied to address the MSS problem, and can be categorized into spectrogram and time-domain based methods.
• However, there is a lack of research of using complementary information of spectrogram and time-domain inputs for MSS.
• In this article, we propose a CatNet framework that concatenates a UNet separation branch using spectrogram as input and a WavUNet separation branch using time-domain waveform as input for MSS.