Optimized phase-space reconstruction for accurate musical-instrument signal classification

Guo, Y; Liu, Q; Wang, A; Sun, C; Tian, W; Naik, GR; Abraham, A

Optimized phase-space reconstruction for accurate musical-instrument signal classification

Guo, Y Liu, Q Wang, A Sun, C Tian, W Naik, GR

Abraham, A

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Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2017, 76 (20), pp. 20719 - 20737
Issue Date:: 2017-10-01

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Field	Value	Language
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Liu, Q	en_US
dc.contributor.author	Wang, A	en_US
dc.contributor.author	Sun, C	en_US
dc.contributor.author	Tian, W	en_US
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Abraham, A	en_US
dc.date.issued	2017-10-01	en_US
dc.identifier.citation	Multimedia Tools and Applications, 2017, 76 (20), pp. 20719 - 20737	en_US
dc.identifier.issn	1380-7501	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124600
dc.description.abstract	© 2016, Springer Science+Business Media New York. Traditional musical-instrument classification methods mainly use regions in the time or/and frequency characteristics, cepstrum characteristics, and MPEG-7 characteristics, and they often lead to erroneous classification. Therefore, there is need to develop a more suitable method that is more applicable to the nonlinear characteristics of musical-instrument signals and can avoid the abovementioned problems. In this paper, a musical-instrument classification method that couples the optimized phase-space reconstruction (OPSR) with a flexible neural tree (FNT) is proposed. As per nonlinear dynamic theory, a principal component analysis and correlation coefficient are used to optimize the phase-space reconstruction (PSR) method. Multidimensional PSR results for different musical-instrument signals are extracted as the main components, and the dimensionality is reduced by the OPSR method. A probability density function (PDF) is introduced in the feature extraction step to differentiate musical instruments according to the phase-space-reconstructible characteristics. A FNT is adopted as a classifier to tackle the variability in musical-instrument signals and to improve the adaptive ability of various target classification problems. Experimental testing has been conducted to show that the proposed OPSR–PDF–FNT algorithm gives superior performance over other comparable algorithms and can classify 12 musical instruments with an accuracy of 98.2 %.	en_US
dc.relation.ispartof	Multimedia Tools and Applications	en_US
dc.relation.isbasedon	10.1007/s11042-016-4021-y	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Optimized phase-space reconstruction for accurate musical-instrument signal classification	en_US
dc.type	Journal Article
utslib.citation.volume	20	en_US
utslib.citation.volume	76	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	20	en_US
pubs.publication-status	Published	en_US
pubs.volume	76	en_US

Abstract:

© 2016, Springer Science+Business Media New York. Traditional musical-instrument classification methods mainly use regions in the time or/and frequency characteristics, cepstrum characteristics, and MPEG-7 characteristics, and they often lead to erroneous classification. Therefore, there is need to develop a more suitable method that is more applicable to the nonlinear characteristics of musical-instrument signals and can avoid the abovementioned problems. In this paper, a musical-instrument classification method that couples the optimized phase-space reconstruction (OPSR) with a flexible neural tree (FNT) is proposed. As per nonlinear dynamic theory, a principal component analysis and correlation coefficient are used to optimize the phase-space reconstruction (PSR) method. Multidimensional PSR results for different musical-instrument signals are extracted as the main components, and the dimensionality is reduced by the OPSR method. A probability density function (PDF) is introduced in the feature extraction step to differentiate musical instruments according to the phase-space-reconstructible characteristics. A FNT is adopted as a classifier to tackle the variability in musical-instrument signals and to improve the adaptive ability of various target classification problems. Experimental testing has been conducted to show that the proposed OPSR–PDF–FNT algorithm gives superior performance over other comparable algorithms and can classify 12 musical instruments with an accuracy of 98.2 %.

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http://hdl.handle.net/10453/124600