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

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dc.contributor.author	Slizovskaia, Olga
dc.contributor.author	Kim, Leo
dc.contributor.author	Haro Ortega, Gloria
dc.contributor.author	Gómez Gutiérrez, Emilia, 1975-
dc.date	2019
dc.identifier.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); 2019 May 12-17; Brighton, United Kingdom. New Jersey: Institute of Electrical and Electronics Engineers; 2019. p. 306-10. DOI: 10.1109/ICASSP.2019.8683800
dc.identifier.citation	2379-190X
dc.identifier.citation	http://dx.doi.org/10.1109/ICASSP.2019.8683800
dc.identifier.uri	http://hdl.handle.net/10230/37258
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	info:eu-repo/grantAgreement/EC/H2020/770376
dc.relation	info:eu-repo/grantAgreement/ES/1PE/TIN2015-70410-C2-1-R
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	© 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
dc.subject	Sound source separation
dc.subject	End-to-end deep learning
dc.subject	Wave-U-Net
dc.title	End-to-end sound source separation conditioned on instrument labels
dc.type	info:eu-repo/semantics/conferenceObject
dc.type	info:eu-repo/semantics/acceptedVersion
dc.description.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.
dc.description.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.
dc.description.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).

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