Multivariate Decomposition of Acoustic Signals in Dispersive Channels

Miloš Brajović (1) , Isidora Stanković (1) , Jonatan Lerga (2) , Cornel Ioana (3) , Eftim Zdravevski (4) , Miloš Daković (1)

1 UCG - University of Montenegro
2 University of Rijeka
3 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
4 UKIM - Ss. Cyril and Methodius University in Skopje

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.

Mots clés: concentration measures; dispersive channels; multivariate signals; non-stationary signals; multicomponent signal decomposition

Domaines: Traitement du signal et de l'image [eess.SP]

Miloš Brajović, Isidora Stanković, Jonatan Lerga, Cornel Ioana, Eftim Zdravevski, et al.. Multivariate Decomposition of Acoustic Signals in Dispersive Channels. Mathematics , 2021, Special Issue New Trends in Graph and Complexity Based Data Analysis and Processing, 9 (21), pp.2796. ⟨10.3390/math9212796⟩. ⟨hal-03589944⟩