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Article

Multivariate Decomposition of Acoustic Signals in Dispersive Channels

1
Faculty of Electrical Engineering, University of Montenegro, 81000 Podgorica, Montenegro
2
Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia
3
Gipsa-Lab, Université Grenoble Alpes, 38400 Grenoble, France
4
Faculty of Computer Science and Engineering, University Ss. Cyril and Methodius, 1000 Skopje, North Macedonia
*
Authors to whom correspondence should be addressed.
Academic Editor: João Nuno Prata
Mathematics 2021, 9(21), 2796; https://doi.org/10.3390/math9212796
Received: 23 September 2021 / Revised: 26 October 2021 / Accepted: 28 October 2021 / Published: 4 November 2021
(This article belongs to the Special Issue New Trends in Graph and Complexity Based Data Analysis and Processing)
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. View Full-Text
Keywords: concentration measures; dispersive channels; multivariate signals; non-stationary signals; multicomponent signal decomposition concentration measures; dispersive channels; multivariate signals; non-stationary signals; multicomponent signal decomposition
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MDPI and ACS Style

Brajović, M.; Stanković, I.; Lerga, J.; Ioana, C.; Zdravevski, E.; Daković, M. Multivariate Decomposition of Acoustic Signals in Dispersive Channels. Mathematics 2021, 9, 2796. https://doi.org/10.3390/math9212796

AMA Style

Brajović M, Stanković I, Lerga J, Ioana C, Zdravevski E, Daković M. Multivariate Decomposition of Acoustic Signals in Dispersive Channels. Mathematics. 2021; 9(21):2796. https://doi.org/10.3390/math9212796

Chicago/Turabian Style

Brajović, Miloš, Isidora Stanković, Jonatan Lerga, Cornel Ioana, Eftim Zdravevski, and Miloš Daković. 2021. "Multivariate Decomposition of Acoustic Signals in Dispersive Channels" Mathematics 9, no. 21: 2796. https://doi.org/10.3390/math9212796

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