Music Source Separation with Generative Flow

Ge Zhu, Jordan Darefsky, Fei Jiang, Anton Selitskiy, Zhiyao Duan. IEEE Signal Processing Letters 2022

Abstract: Full supervision models for source separation are trained on mixture-source parallel data and have achieved superior performance in recent years. However, large-scale and naturally mixed parallel training data are difficult to obtain for music, and such models are difficult to adapt to mixtures with new sources. Source-only supervision models, in contrast, only require clean sources for training; They learn source models and then apply these models to separate the mixture.

[Code][IEEE SPL][Demo]

  author={Zhu, Ge and Darefsky, Jordan and Jiang, Fei and Selitskiy, Anton and Duan, Zhiyao},
  journal={IEEE Signal Processing Letters}, 
  title={Music Source Separation With Generative Flow},