Multivariate Decomposition of Acoustic Signals in Dispersive Channels

We present a signal decomposition procedure, which separates modes into individual components while preserving their integrity, in effort to tackle the challenges related to the characterization of modes in an acoustic dispersive environment. With this approach, each mode can be analyzed and process...
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Autor*in:	Miloš Brajović [verfasserIn] Isidora Stanković [verfasserIn] Jonatan Lerga [verfasserIn] Cornel Ioana [verfasserIn] Eftim Zdravevski [verfasserIn] Miloš Daković [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	concentration measures dispersive channels multivariate signals non-stationary signals multicomponent signal decomposition

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 9(2021), 21, p 2796
Übergeordnetes Werk:	volume:9 ; year:2021 ; number:21, p 2796

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DOI / URN:	10.3390/math9212796

Katalog-ID:	DOAJ072378433

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520			\|a We present a signal decomposition procedure, which separates modes into individual components while preserving their integrity, in effort to tackle the challenges related to the characterization of modes in an acoustic dispersive environment. With this approach, each mode can be analyzed and processed individually, which carries opportunities for new insights into their characterization possibilities. The proposed methodology is based on the eigenanalysis of the autocorrelation matrix of the analyzed signal. When eigenvectors of this matrix are properly linearly combined, each signal component can be separately reconstructed. A proper linear combination is determined based on the minimization of concentration measures calculated exploiting time-frequency representations. In this paper, we engage a steepest-descent-like algorithm for the minimization process. Numerical results support the theory and indicate the applicability of the proposed methodology in the decomposition of acoustic signals in dispersive channels.
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allfieldsGer	10.3390/math9212796 doi (DE-627)DOAJ072378433 (DE-599)DOAJ76910dbdc91f48b391486fa2ec60bc63 DE-627 ger DE-627 rakwb eng QA1-939 Miloš Brajović verfasserin aut Multivariate Decomposition of Acoustic Signals in Dispersive Channels 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a signal decomposition procedure, which separates modes into individual components while preserving their integrity, in effort to tackle the challenges related to the characterization of modes in an acoustic dispersive environment. With this approach, each mode can be analyzed and processed individually, which carries opportunities for new insights into their characterization possibilities. The proposed methodology is based on the eigenanalysis of the autocorrelation matrix of the analyzed signal. When eigenvectors of this matrix are properly linearly combined, each signal component can be separately reconstructed. A proper linear combination is determined based on the minimization of concentration measures calculated exploiting time-frequency representations. In this paper, we engage a steepest-descent-like algorithm for the minimization process. Numerical results support the theory and indicate the applicability of the proposed methodology in the decomposition of acoustic signals in dispersive channels. concentration measures dispersive channels multivariate signals non-stationary signals multicomponent signal decomposition Mathematics Isidora Stanković verfasserin aut Jonatan Lerga verfasserin aut Cornel Ioana verfasserin aut Eftim Zdravevski verfasserin aut Miloš Daković verfasserin aut In Mathematics MDPI AG, 2013 9(2021), 21, p 2796 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:9 year:2021 number:21, p 2796 https://doi.org/10.3390/math9212796 kostenfrei https://doaj.org/article/76910dbdc91f48b391486fa2ec60bc63 kostenfrei https://www.mdpi.com/2227-7390/9/21/2796 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 21, p 2796
allfieldsSound	10.3390/math9212796 doi (DE-627)DOAJ072378433 (DE-599)DOAJ76910dbdc91f48b391486fa2ec60bc63 DE-627 ger DE-627 rakwb eng QA1-939 Miloš Brajović verfasserin aut Multivariate Decomposition of Acoustic Signals in Dispersive Channels 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a signal decomposition procedure, which separates modes into individual components while preserving their integrity, in effort to tackle the challenges related to the characterization of modes in an acoustic dispersive environment. With this approach, each mode can be analyzed and processed individually, which carries opportunities for new insights into their characterization possibilities. The proposed methodology is based on the eigenanalysis of the autocorrelation matrix of the analyzed signal. When eigenvectors of this matrix are properly linearly combined, each signal component can be separately reconstructed. A proper linear combination is determined based on the minimization of concentration measures calculated exploiting time-frequency representations. In this paper, we engage a steepest-descent-like algorithm for the minimization process. Numerical results support the theory and indicate the applicability of the proposed methodology in the decomposition of acoustic signals in dispersive channels. concentration measures dispersive channels multivariate signals non-stationary signals multicomponent signal decomposition Mathematics Isidora Stanković verfasserin aut Jonatan Lerga verfasserin aut Cornel Ioana verfasserin aut Eftim Zdravevski verfasserin aut Miloš Daković verfasserin aut In Mathematics MDPI AG, 2013 9(2021), 21, p 2796 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:9 year:2021 number:21, p 2796 https://doi.org/10.3390/math9212796 kostenfrei https://doaj.org/article/76910dbdc91f48b391486fa2ec60bc63 kostenfrei https://www.mdpi.com/2227-7390/9/21/2796 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 21, p 2796
language	English
source	In Mathematics 9(2021), 21, p 2796 volume:9 year:2021 number:21, p 2796
sourceStr	In Mathematics 9(2021), 21, p 2796 volume:9 year:2021 number:21, p 2796
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topic_facet	concentration measures dispersive channels multivariate signals non-stationary signals multicomponent signal decomposition Mathematics
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authorswithroles_txt_mv	Miloš Brajović @@aut@@ Isidora Stanković @@aut@@ Jonatan Lerga @@aut@@ Cornel Ioana @@aut@@ Eftim Zdravevski @@aut@@ Miloš Daković @@aut@@
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callnumber-first	Q - Science
author	Miloš Brajović
spellingShingle	Miloš Brajović misc QA1-939 misc concentration measures misc dispersive channels misc multivariate signals misc non-stationary signals misc multicomponent signal decomposition misc Mathematics Multivariate Decomposition of Acoustic Signals in Dispersive Channels
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topic_title	QA1-939 Multivariate Decomposition of Acoustic Signals in Dispersive Channels concentration measures dispersive channels multivariate signals non-stationary signals multicomponent signal decomposition
topic	misc QA1-939 misc concentration measures misc dispersive channels misc multivariate signals misc non-stationary signals misc multicomponent signal decomposition misc Mathematics
topic_unstemmed	misc QA1-939 misc concentration measures misc dispersive channels misc multivariate signals misc non-stationary signals misc multicomponent signal decomposition misc Mathematics
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title	Multivariate Decomposition of Acoustic Signals in Dispersive Channels
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title_sort	multivariate decomposition of acoustic signals in dispersive channels
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title_auth	Multivariate Decomposition of Acoustic Signals in Dispersive Channels
abstract	We present a signal decomposition procedure, which separates modes into individual components while preserving their integrity, in effort to tackle the challenges related to the characterization of modes in an acoustic dispersive environment. With this approach, each mode can be analyzed and processed individually, which carries opportunities for new insights into their characterization possibilities. The proposed methodology is based on the eigenanalysis of the autocorrelation matrix of the analyzed signal. When eigenvectors of this matrix are properly linearly combined, each signal component can be separately reconstructed. A proper linear combination is determined based on the minimization of concentration measures calculated exploiting time-frequency representations. In this paper, we engage a steepest-descent-like algorithm for the minimization process. Numerical results support the theory and indicate the applicability of the proposed methodology in the decomposition of acoustic signals in dispersive channels.
abstractGer	We present a signal decomposition procedure, which separates modes into individual components while preserving their integrity, in effort to tackle the challenges related to the characterization of modes in an acoustic dispersive environment. With this approach, each mode can be analyzed and processed individually, which carries opportunities for new insights into their characterization possibilities. The proposed methodology is based on the eigenanalysis of the autocorrelation matrix of the analyzed signal. When eigenvectors of this matrix are properly linearly combined, each signal component can be separately reconstructed. A proper linear combination is determined based on the minimization of concentration measures calculated exploiting time-frequency representations. In this paper, we engage a steepest-descent-like algorithm for the minimization process. Numerical results support the theory and indicate the applicability of the proposed methodology in the decomposition of acoustic signals in dispersive channels.
abstract_unstemmed	We present a signal decomposition procedure, which separates modes into individual components while preserving their integrity, in effort to tackle the challenges related to the characterization of modes in an acoustic dispersive environment. With this approach, each mode can be analyzed and processed individually, which carries opportunities for new insights into their characterization possibilities. The proposed methodology is based on the eigenanalysis of the autocorrelation matrix of the analyzed signal. When eigenvectors of this matrix are properly linearly combined, each signal component can be separately reconstructed. A proper linear combination is determined based on the minimization of concentration measures calculated exploiting time-frequency representations. In this paper, we engage a steepest-descent-like algorithm for the minimization process. Numerical results support the theory and indicate the applicability of the proposed methodology in the decomposition of acoustic signals in dispersive channels.
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Multivariate Decomposition of Acoustic Signals in Dispersive Channels

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