End-to-end sound source separation conditioned on instrument labels
- 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
- 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  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.
- 11 times cited Scopus
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