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Dynamic Independent Component/Vector Analysis: Time-Variant Linear Mixtures Separable by Time-Invariant Beamformers
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SYSNO ASEP 0542013 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Dynamic Independent Component/Vector Analysis: Time-Variant Linear Mixtures Separable by Time-Invariant Beamformers Author(s) Koldovský, Z. (CZ)
Kautský, V. (CZ)
Tichavský, Petr (UTIA-B) RID, ORCID
Čmejla, J. (CZ)
Málek, J. (CZ)Number of authors 5 Source Title IEEE Transactions on Signal Processing - ISSN 1053-587X
Roč. 69, č. 1 (2021), s. 2158-2173Number of pages 16 s. Publication form Print - P Language eng - English Country US - United States Keywords Blind Source Separation ; Blind Source Extraction ; Independent Vector Analysis Subject RIV BI - Acoustics OECD category Electrical and electronic engineering Method of publishing Limited access Institutional support UTIA-B - RVO:67985556 UT WOS 000645052600001 EID SCOPUS 85103266969 DOI 10.1109/TSP.2021.3068626 Annotation A novel extension of Independent Component and Independent Vector Analysis for blind extraction/separation of one or several sources from time-varying mixtures is proposed. The mixtures are assumed to be separable source-by-source in series or in parallel based on a recently proposed mixing model that allows for the movements of the desired source while the separating beamformer is time-invariant. The popular FastICA algorithm is extended for these mixtures in one-unit, symmetric and block-deflation variants. The algorithms are derived within a unified framework so that they are applicable in the real-valued as well as complex-valued domains, and jointly to several mixtures, similar to Independent Vector Analysis. Performance analysis of the one-unit algorithm is provided, it shows its asymptotic efficiency under the given mixing and statistical models. Numerical simulations corroborate the validity of the analysis, confirm the usefulness of the algorithms in separation of moving sources, and show the superior speed of convergence and ability to separate super-Gaussian as well as sub-Gaussian signals. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2022 Electronic address https://ieeexplore.ieee.org/document/9387552
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