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.ca
- dc.contributor.author Cobos, Máximoca
- dc.contributor.author Vera Candeas, Pedroca
- dc.contributor.author Rodríguez Serrano, Francisco Jca
- dc.date.accessioned 2015-03-18T09:12:04Z
- dc.date.available 2015-03-18T09:12:04Z
- dc.date.issued 2013ca
- 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.en
- 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-C02-02en
- dc.format.mimetype application/pdfca
- 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 10.1186/1687-6180-2013-184ca
- dc.identifier.doi http://dx.doi.org/10.1186/1687-6180-2013-184
- dc.identifier.issn 1687-6172ca
- dc.identifier.uri http://hdl.handle.net/10230/23218
- dc.language.iso engca
- dc.publisher SpringerOpenca
- dc.relation.ispartof EURASIP Journal on Advances in Signal Processing. 2013; 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 © 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.ca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri http://creativecommons.org/licenses/by/2.0
- dc.subject.other Micròfonsca
- dc.subject.other Instruments musicalsca
- dc.subject.other Música -- Informàticaca
- dc.title Nonnegative signal factorization with learnt instrument models for sound source separation in close-microphone recordingsen
- dc.type info:eu-repo/semantics/articleca
- dc.type.version info:eu-repo/semantics/publishedVersionca