Online Singing Voice Separation Using a Recurrent One-dimensional U-NET Trained with Deep Feature Losses

Clement S. J. Doire

ICASSP 2019accepted0 citations

Online Singing Voice Separation Using a Recurrent One-dimensional U-NET Trained with Deep Feature Losses

Abstract

This paper proposes an online approach to the singing voice separation problem. Based on a combination of one-dimensional convolutional layers along the frequency axis and recurrent layers to enforce temporal coherency, state-of-the-art performance is achieved. The concept of using deep features in the loss function to guide training and improve the model's performance is also investigated.

BibTeX

@inproceedings{icassp2019_onlinesingingvoi,
  title = {Online Singing Voice Separation Using a Recurrent One-dimensional U-NET Trained with Deep Feature Losses},
  author = {Clement S. J. Doire},
  booktitle = {ICASSP 2019},
  year = {2019}
}