Parametric Time-Frequency Analysis and Its Applications in Music Classification

Abstract Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but t...
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Autor*in:	Shen, Ying [verfasserIn] Li, Xiaoli [verfasserIn] Ma, Ngok-Wah [verfasserIn] Krishnan, Sridhar [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Linear Discriminant Analysis High Frequency Range Discriminant Feature Match Pursuit Discriminant Information

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2010(2010), 1 vom: 27. Sept.
Übergeordnetes Werk:	volume:2010 ; year:2010 ; number:1 ; day:27 ; month:09

Links:	Volltext

DOI / URN:	10.1155/2010/380349

Katalog-ID:	SPR031993877

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spelling	10.1155/2010/380349 doi (DE-627)SPR031993877 (SPR)380349-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Shen, Ying verfasserin aut Parametric Time-Frequency Analysis and Its Applications in Music Classification 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications. Linear Discriminant Analysis (dpeaa)DE-He213 High Frequency Range (dpeaa)DE-He213 Discriminant Feature (dpeaa)DE-He213 Match Pursuit (dpeaa)DE-He213 Discriminant Information (dpeaa)DE-He213 Li, Xiaoli verfasserin aut Ma, Ngok-Wah verfasserin aut Krishnan, Sridhar verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 27. Sept. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:27 month:09 https://dx.doi.org/10.1155/2010/380349 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 27 09
allfields_unstemmed	10.1155/2010/380349 doi (DE-627)SPR031993877 (SPR)380349-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Shen, Ying verfasserin aut Parametric Time-Frequency Analysis and Its Applications in Music Classification 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications. Linear Discriminant Analysis (dpeaa)DE-He213 High Frequency Range (dpeaa)DE-He213 Discriminant Feature (dpeaa)DE-He213 Match Pursuit (dpeaa)DE-He213 Discriminant Information (dpeaa)DE-He213 Li, Xiaoli verfasserin aut Ma, Ngok-Wah verfasserin aut Krishnan, Sridhar verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 27. Sept. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:27 month:09 https://dx.doi.org/10.1155/2010/380349 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 27 09
allfieldsGer	10.1155/2010/380349 doi (DE-627)SPR031993877 (SPR)380349-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Shen, Ying verfasserin aut Parametric Time-Frequency Analysis and Its Applications in Music Classification 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications. Linear Discriminant Analysis (dpeaa)DE-He213 High Frequency Range (dpeaa)DE-He213 Discriminant Feature (dpeaa)DE-He213 Match Pursuit (dpeaa)DE-He213 Discriminant Information (dpeaa)DE-He213 Li, Xiaoli verfasserin aut Ma, Ngok-Wah verfasserin aut Krishnan, Sridhar verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 27. Sept. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:27 month:09 https://dx.doi.org/10.1155/2010/380349 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 27 09
allfieldsSound	10.1155/2010/380349 doi (DE-627)SPR031993877 (SPR)380349-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Shen, Ying verfasserin aut Parametric Time-Frequency Analysis and Its Applications in Music Classification 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications. Linear Discriminant Analysis (dpeaa)DE-He213 High Frequency Range (dpeaa)DE-He213 Discriminant Feature (dpeaa)DE-He213 Match Pursuit (dpeaa)DE-He213 Discriminant Information (dpeaa)DE-He213 Li, Xiaoli verfasserin aut Ma, Ngok-Wah verfasserin aut Krishnan, Sridhar verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 27. Sept. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:27 month:09 https://dx.doi.org/10.1155/2010/380349 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 27 09
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2010(2010), 1 vom: 27. Sept. volume:2010 year:2010 number:1 day:27 month:09
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topic_facet	Linear Discriminant Analysis High Frequency Range Discriminant Feature Match Pursuit Discriminant Information
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author	Shen, Ying
spellingShingle	Shen, Ying ddc 620 bkl 53.73 misc Linear Discriminant Analysis misc High Frequency Range misc Discriminant Feature misc Match Pursuit misc Discriminant Information Parametric Time-Frequency Analysis and Its Applications in Music Classification
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title	Parametric Time-Frequency Analysis and Its Applications in Music Classification
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title_full	Parametric Time-Frequency Analysis and Its Applications in Music Classification
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title_sort	parametric time-frequency analysis and its applications in music classification
title_auth	Parametric Time-Frequency Analysis and Its Applications in Music Classification
abstract	Abstract Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.
abstractGer	Abstract Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.
abstract_unstemmed	Abstract Analysis of nonstationary signals, such as music signals, is a challenging task. The purpose of this study is to explore an efficient and powerful technique to analyze and classify music signals in higher frequency range (44.1 kHz). The pursuit methods are good tools for this purpose, but they aimed at representing the signals rather than classifying them as in Y. Paragakin et al., 2009. Among the pursuit methods, matching pursuit (MP), an adaptive true nonstationary time-frequency signal analysis tool, is applied for music classification. First, MP decomposes the sample signals into time-frequency functions or atoms. Atom parameters are then analyzed and manipulated, and discriminant features are extracted from atom parameters. Besides the parameters obtained using MP, an additional feature, central energy, is also derived. Linear discriminant analysis and the leave-one-out method are used to evaluate the classification accuracy rate for different feature sets. The study is one of the very few works that analyze atoms statistically and extract discriminant features directly from the parameters. From our experiments, it is evident that the MP algorithm with the Gabor dictionary decomposes nonstationary signals, such as music signals, into atoms in which the parameters contain strong discriminant information sufficient for accurate and efficient signal classifications.
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title_short	Parametric Time-Frequency Analysis and Its Applications in Music Classification
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Parametric Time-Frequency Analysis and Its Applications in Music Classification

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