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

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Title:	End-to-end sound source separation conditioned on instrument labels
Author:	Slizovskaia, Olga; Kim, Leo; Haro Ortega, Gloria; Gómez Gutiérrez, Emilia, 1975-
Abstract:	Comunicació presentada a: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing celebrat de 12 al 17 de maig de 2019 a Brighton, Regne Unit.
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.
Abstract:	This work has received funding from the Maria de Maeztu Programme (MDM-2015-0502), ERC Innovation Programme (grant 770376, TROMPA), and MINECO/FEDER UE project (TIN2015-70410-C2-1-R).
Subject(s):	-Sound source separation -End-to-end deep learning -Wave-U-Net
Rights:	info:eu-repo/semantics/embargoedAccess © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/ICASSP.2019.8683800
Document type:	Conference Object Article - Accepted version
Published by:	Institute of Electrical and Electronics Engineers (IEEE)
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