Dct based feature extraction and support vector machine classification for musical instruments tone recognition

Sumarno, L; Chai, R

Dct based feature extraction and support vector machine classification for musical instruments tone recognition

Sumarno, L Chai, R

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Publisher:: Institute of Advanced Engineering and Science
Publication Type:: Journal Article
Citation:: Bulletin of Electrical Engineering and Informatics, 2021, 10, (5), pp. 2796-2803
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Sumarno, L
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024
dc.date.accessioned	2022-04-11T05:59:02Z
dc.date.available	2022-04-11T05:59:02Z
dc.date.issued	2021-10-01
dc.identifier.citation	Bulletin of Electrical Engineering and Informatics, 2021, 10, (5), pp. 2796-2803
dc.identifier.issn	2089-3191
dc.identifier.issn	2302-9285
dc.identifier.uri	http://hdl.handle.net/10453/156064
dc.description.abstract	The conducted research proposes a feature extraction and classification combination method that is used in a tone recognition system for musical instruments. It is expected that by implementing this combination, the tone recognition system will require fewer feature extraction coefficients than those previously investigated. The proposed combination comprises of feature extraction using discrete cosine transform (DCT) and classification using support vector machine (SVM). Bellyra, clarinet, and pianica tones were used in the experiment, with each indicating a tone with one, several, or many major local peaks in the transform domain. Based on the results of the tests, the proposed combination is efficient enough to be used in a tone recognition system for musical instruments. This is indicated in recognizing a tone, it only needs at least eight feature extraction coefficients.
dc.language	en
dc.publisher	Institute of Advanced Engineering and Science
dc.relation.ispartof	Bulletin of Electrical Engineering and Informatics
dc.relation.isbasedon	10.11591/eei.v10i5.3158
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Dct based feature extraction and support vector machine classification for musical instruments tone recognition
dc.type	Journal Article
utslib.citation.volume	10
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2022-04-11T05:59:01Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	5

Abstract:

The conducted research proposes a feature extraction and classification combination method that is used in a tone recognition system for musical instruments. It is expected that by implementing this combination, the tone recognition system will require fewer feature extraction coefficients than those previously investigated. The proposed combination comprises of feature extraction using discrete cosine transform (DCT) and classification using support vector machine (SVM). Bellyra, clarinet, and pianica tones were used in the experiment, with each indicating a tone with one, several, or many major local peaks in the transform domain. Based on the results of the tests, the proposed combination is efficient enough to be used in a tone recognition system for musical instruments. This is indicated in recognizing a tone, it only needs at least eight feature extraction coefficients.

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http://hdl.handle.net/10453/156064