Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/2422
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dc.contributor.authorBhalke, D.G.-
dc.contributor.authorRama Rao, C.B-
dc.contributor.authorBormane, D.S.-
dc.date.accessioned2025-01-06T05:05:18Z-
dc.date.available2025-01-06T05:05:18Z-
dc.date.issued2014-
dc.identifier.citation10.1109/spin.2014.6776918en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2422-
dc.descriptionNITWen_US
dc.description.abstractThis paper presents classification and recognition of monophonic isolated musical instrument sounds using higher order spectra such as Bispectrum and Trispectrum. Experimental results on a widely used dataset shows that higher order spectra based features improve the recognition accuracy, when combined with conventional features such as Mel Frequency Cepstral Coefficient (MFCC), Cepstral, Spectral and Temporal features. Nineteen western musical instruments covering four families with full pitch range have been used for experimentation.en_US
dc.language.isoenen_US
dc.publisher2014 International Conference on Signal Processing and Integrated Networks, SPIN 2014en_US
dc.subjectBispectrum,en_US
dc.subjectTrispectrum,en_US
dc.titleMusical Instrument Classification using Higher Order Spectraen_US
dc.typeOtheren_US
Appears in Collections:Electronics and Communication Engineering

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