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Comparison of features in musical instrument identification using artificial neural networks

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dc.contributor.author Loughran, Róisín
dc.contributor.author Walker, Jacqueline
dc.contributor.author O'Farrell, Marion
dc.contributor.author O'Neill, Michael
dc.date.accessioned 2009-03-13T14:13:54Z
dc.date.available 2009-03-13T14:13:54Z
dc.date.issued 2008
dc.identifier.uri http://hdl.handle.net/10344/144
dc.description Non-peer-reviewed
dc.description.abstract This paper examines the use of a number of auditory features in identifying musical instruments. The Temporal Envelope, Centroid, Melfrequency Cepstral Coefficients (MFCCs), Inharmonicity, Spectral Irregularity and Number of Spectral Peaks are all examined. By using these features to train a Multi-Layered Perceptron (MLP), it is determined that the MFCCs are the most efficient of these features in musical instrument identification. The Inharmonicity, Spectral Irregularity and Number of Spectral Peaks offered no benefit to the classifier. Of the instruments studied, the piano was most accurately classified and the violin was the least accurately classified instrument.
dc.language.iso eng en
dc.publisher Springer en
dc.relation.ispartofseries 5th International Symposium on Computer Music Modeling and Retrieval pp. 19-33
dc.subject musical instrument en
dc.title Comparison of features in musical instrument identification using artificial neural networks en
dc.type Conference item en
dc.type.supercollection all_ul_research en
dc.type.restriction none en
dc.identifier.local 09ece06
dc.contributor.sponsor SFI
dc.internal.rssid 1396985


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