Conditioned source separation for musical instrument performances

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 pape...

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Detalles Bibliográficos
Autores: Slizovskaia, Olga, Haro Ortega, Gloria, Gómez Gutiérrez, Emilia, 1975-
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/47693
Acceso en línea:http://hdl.handle.net/10230/47693
http://dx.doi.org/10.1109/TASLP.2021.3082331
Access Level:acceso abierto
Palabra clave:Single Channel Source Separation
Audio-Visual Analysis
Conditioned Neural Networks
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spelling Conditioned source separation for musical instrument performancesSlizovskaia, OlgaHaro Ortega, GloriaGómez Gutiérrez, Emilia, 1975-Single Channel Source SeparationAudio-Visual AnalysisConditioned Neural NetworksIn 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.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.Institute of Electrical and Electronics Engineers (IEEE)20212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/47693http://dx.doi.org/10.1109/TASLP.2021.3082331reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésIEEE/ACM Transactions on Audio, Speech, and Language Processing. 2021;29:2083-95info:eu-repo/grantAgreement/EC/H2020/777826info:eu-repo/grantAgreement/ES/2PE/PGC2018-098625-B-I00info:eu-repo/grantAgreement/EC/FP7/770376© 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.2021.3082331info:eu-repo/semantics/openAccessoai:recercat.cat:10230/476932026-05-29T05:05:01Z
dc.title.none.fl_str_mv Conditioned source separation for musical instrument performances
title Conditioned source separation for musical instrument performances
spellingShingle Conditioned source separation for musical instrument performances
Slizovskaia, Olga
Single Channel Source Separation
Audio-Visual Analysis
Conditioned Neural Networks
title_short Conditioned source separation for musical instrument performances
title_full Conditioned source separation for musical instrument performances
title_fullStr Conditioned source separation for musical instrument performances
title_full_unstemmed Conditioned source separation for musical instrument performances
title_sort Conditioned source separation for musical instrument performances
dc.creator.none.fl_str_mv Slizovskaia, Olga
Haro Ortega, Gloria
Gómez Gutiérrez, Emilia, 1975-
author Slizovskaia, Olga
author_facet Slizovskaia, Olga
Haro Ortega, Gloria
Gómez Gutiérrez, Emilia, 1975-
author_role author
author2 Haro Ortega, Gloria
Gómez Gutiérrez, Emilia, 1975-
author2_role author
author
dc.subject.none.fl_str_mv Single Channel Source Separation
Audio-Visual Analysis
Conditioned Neural Networks
topic Single Channel Source Separation
Audio-Visual Analysis
Conditioned Neural Networks
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/47693
http://dx.doi.org/10.1109/TASLP.2021.3082331
url http://hdl.handle.net/10230/47693
http://dx.doi.org/10.1109/TASLP.2021.3082331
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2021;29:2083-95
info:eu-repo/grantAgreement/EC/H2020/777826
info:eu-repo/grantAgreement/ES/2PE/PGC2018-098625-B-I00
info:eu-repo/grantAgreement/EC/FP7/770376
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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