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Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings

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dc.contributor.author Carabias Orti, Julio J.
dc.contributor.author Cobos, Máximo
dc.contributor.author Vera Candeas, Pedro
dc.contributor.author Rodríguez Serrano, Francisco J
dc.date.accessioned 2015-03-18T09:12:04Z
dc.date.available 2015-03-18T09:12:04Z
dc.date.issued 2013
dc.identifier.citation Carabias-Orti JJ, Cobos M, Vera-Candeas P, Rodríguez-Serrano FJ. Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings. EURASIP Journal on Advances in Signal Processing. 2013; 2013: 184. DOI 10.1186/1687-6180-2013-184
dc.identifier.issn 1687-6172
dc.identifier.uri http://hdl.handle.net/10230/23218
dc.description.abstract Close-microphone techniques are extensively employed in many live music recordings, allowing for interference rejection and reducing the amount of reverberation in the resulting instrument tracks. However, despite the use of directional microphones, the recorded tracks are not completely free from source interference, a problem which is commonly known as microphone leakage. While source separation methods are potentially a solution to this problem, few approaches take into account the huge amount of prior information available in this scenario. In fact, besides the special properties of close-microphone tracks, the knowledge on the number and type of instruments making up the mixture can also be successfully exploited for improved separation performance. In this paper, a nonnegative matrix factorization (NMF) method making use of all the above information is proposed. To this end, a set of instrument models are learnt from a training database and incorporated into a multichannel extension of the NMF algorithm. Several options to initialize the algorithm are suggested, exploring their performance in multiple music tracks and comparing the results to other state-of-the-art approaches.
dc.description.sponsorship This work was supported by the Andalusian Business, Science and Innovation Council under project P2010- TIC-6762, (FEDER) the Spanish Ministry of Economy and Competitiveness under the projects TEC2012-38142-C04-03 and TEC2012-37945-C02-02
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher SpringerOpen
dc.relation.ispartof EURASIP Journal on Advances in Signal Processing. 2013; 2013: 184
dc.rights © 2013 Carabias-Orti et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.uri http://creativecommons.org/licenses/by/2.0
dc.subject.other Micròfons
dc.subject.other Instruments musicals
dc.subject.other Música -- Informàtica
dc.title Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordings
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1186/1687-6180-2013-184
dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/TEC2012-37945
dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/TEC2012-38142
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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