Musical Instrument Classification Based on Nonlinear Recurrence Analysis and Supervised Learning

dc.contributor.authorRui, Rui
dc.contributor.authorBao, Changchun
dc.coverage.issue1cs
dc.coverage.volume22cs
dc.date.accessioned2015-01-20T14:14:13Z
dc.date.available2015-01-20T14:14:13Z
dc.date.issued2013-04cs
dc.description.abstractIn this paper, the phase space reconstruction of time series produced by different instruments is discussed based on the nonlinear dynamic theory. The dense ratio, a novel quantitative recurrence parameter, is proposed to describe the difference of wind instruments, stringed instruments and keyboard instruments in the phase space by analyzing the recursive property of every instrument. Furthermore, a novel supervised learning algorithm for automatic classification of individual musical instrument signals is addressed deriving from the idea of supervised non-negative matrix factorization (NMF) algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which NMF is unable to do. The experimental results indicate that the accuracy of the proposed method is improved by 3% comparing with the conventional features in the individual instrument classification.en
dc.formattextcs
dc.format.extent60-67cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2013, vol. 22, č. 1, s. 60-67. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/36801
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2013/13_01_0060_0067.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectPhase space reconstructionen
dc.subjectrecurrence analysisen
dc.subjectdense ratioen
dc.subjectsupervised learningen
dc.subjectmusical instrument classificationen
dc.titleMusical Instrument Classification Based on Nonlinear Recurrence Analysis and Supervised Learningen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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