End-to-end Sound Source Separation Conditioned on Instrument Labels

Olga Slizovskaia; Leo Kim; Gloria Haro; Emilia Gómez

ICASSP 2019accepted0 citations

End-to-end Sound Source Separation Conditioned on Instrument Labels

Olga Slizovskaia, Leo Kim, Gloria Haro, Emilia Gómez

Abstract

Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model? This paper presents an extension of the Wave-U-Net [1] model which allows end-to-end monaural source separation with a non-fixed number of sources. Furthermore, we propose multiplicative conditioning with instrument labels at the bottleneck of the Wave-U-Net and show its effect on the separation results. This approach can be further extended to other types of conditioning such as audio-visual source separation and score-informed source separation.

BibTeX

@inproceedings{icassp2019_endtoendsoundsou,
  title = {End-to-end Sound Source Separation Conditioned on Instrument Labels},
  author = {Olga Slizovskaia and Leo Kim and Gloria Haro and Emilia Gómez},
  booktitle = {ICASSP 2019},
  year = {2019}
}