End-to-end sound source separation conditioned on instrument labels

  • Authors
  • Slizovskaia O, Kim L, Haro G, Gomez E
  • UPF authors
  • HARO ORTEGA, GLORIA;
  • Authors of the book
  • -
  • Book title
  • 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Publication year
  • 2019
  • Pages
  • 306-310
  • 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-UNet [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.
  • Complete citation
  • Slizovskaia O, Kim L, Haro G, Gomez E. End-to-end sound source separation conditioned on instrument labels. In: -. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1 ed. 2019. p. 306-310.
Bibliometric indicators
  • 11 times cited Scopus
  • Índex Scimago de 0