IEEE International Conference on Acoustics, Speech, and Signal Processing
Publication year
2019
Pages
0-0
ISSN
1520-6149
Publication State
Published
Abstract
Can we perform an end-to-end sound source separation (SSS) with a variable number of sources using a deep learning model? This paper presents an extension of the Wave-U-Net 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 SSS and score-informed SSS.
Complete citation
Slizovskaia, O.; Kim L.; Haro G.; Gómez E.. End-to-End Sound Source Separation Conditioned On Instrument Labels. IEEE International Conference on Acoustics, Speech, and Signal Processing 2019; ( ).