Multivariate Decomposition of Acoustic Signals in Dispersive Channels

Miloš Brajović, Isidora Stanković, Jonatan Lerga, Cornel Ioana, Eftim Zdravevski and Miloš Daković
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Miloš Brajović: Faculty of Electrical Engineering, University of Montenegro, 81000 Podgorica, Montenegro
Isidora Stanković: Faculty of Electrical Engineering, University of Montenegro, 81000 Podgorica, Montenegro
Jonatan Lerga: Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia
Cornel Ioana: Gipsa-Lab, Université Grenoble Alpes, 38400 Grenoble, France
Eftim Zdravevski: Faculty of Computer Science and Engineering, University Ss. Cyril and Methodius, 1000 Skopje, North Macedonia
Miloš Daković: Faculty of Electrical Engineering, University of Montenegro, 81000 Podgorica, Montenegro

Mathematics, 2021, vol. 9, issue 21, 1-29

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.

Keywords: concentration measures; dispersive channels; multivariate signals; non-stationary signals; multicomponent signal decomposition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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