Conditioned source separation for musical instrument performances

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• dc.contributor.author Slizovskaia, Olga
• dc.contributor.author Haro Ortega, Gloria
• dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
• dc.date.accessioned 2021-05-31T07:41:21Z
• dc.date.issued 2021
• dc.description.abstract In music source separation, the number of sources may vary for each piece and some of the sources may belong to the same family of instruments, thus sharing timbral characteristics and making the sources more correlated. This leads to additional challenges in the source separation problem. This paper proposes a source separation method for multiple musical instruments sounding simultaneously and explores how much additional information apart from the audio stream can lift the quality of source separation. We explore conditioning techniques at different levels of a primary source separation network and utilize two extra modalities of data, namely presence or absence of instruments in the mixture, and the corresponding video stream data.
• dc.description.sponsorship This work was funded in part by ERC Innovation Programme (grant 770376, TROMPA); Spanish Ministry of Economy and Competitiveness under the Mar´ıa de Maeztu Units of Excellence Program (MDM-2015-0502) and the Social European Funds; the MICINN/FEDER UE project with reference PGC2018-098625-B-I00; and the H2020-MSCARISE-2017 project with reference 777826 NoMADS. We gratefully acknowledge NVIDIA for the donation of GPUs used for the experiments.
• dc.format.mimetype application/pdf
• dc.identifier.citation Slizovskaia O, Haro G, Gomez E. Conditioned source separation for musical instrument performances. IEEE/ACM Trans. Audio, Speech, Language Process. 2021;29:2083-95. DOI: 10.1109/TASLP.2021.3082331
• dc.identifier.doi http://dx.doi.org/10.1109/TASLP.2021.3082331
• dc.identifier.issn 2329-9290
• dc.identifier.uri http://hdl.handle.net/10230/47693
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2021;29:2083-95
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/777826
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PGC2018-098625-B-I00
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/770376
• dc.rights © 2021 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/TASLP.2021.3082331
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Single Channel Source Separation
• dc.subject.keyword Audio-Visual Analysis
• dc.subject.keyword Conditioned Neural Networks
• dc.title Conditioned source separation for musical instrument performances
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion