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The use of mel-frequency cepstral coefficients in musical instrument identification

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Show simple item record Loughran, Róisín Walker, Jacqueline O'Neill, Michael O'Farrell, Marion 2018-02-23T16:01:45Z 2018-02-23T16:01:45Z 2008
dc.description peer-reviewed en_US
dc.description.abstract This paper examines the use of Mel-frequency Cepstral Coefficients in the classification of musical instruments. 2004 piano, violin and flute samples are analysed to get their coefficients. These coefficients are reduced using principal component analysis and used to train a multi-layered perceptron. The network is trained on the first 3, 4 and 5 principal components calculated from the envelope of the changes in the coefficients. This trained network is then used to classify novel input samples. By training and testing the network on a different number of coefficients, the optimum number of coefficients to include for identifying a musical instrument is determined. We conclude that using 4 principal components from the first 15 coefficients gives the most accurate classification results. en_US
dc.language.iso eng en_US
dc.publisher International Computer Music Association en_US
dc.relation.ispartof International Computer Music Conference en
dc.relation.ispartofseries International Computer Music Conference;387-390
dc.subject music en_US
dc.subject sound en_US
dc.title The use of mel-frequency cepstral coefficients in musical instrument identification en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US 2018-02-23T15:56:18Z
dc.description.version PUBLISHED
dc.contributor.sponsor SFI en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1450166
dc.internal.copyrightchecked Yes
dc.description.status non-peer-reviewed

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