Dynamic independent component extraction with blending mixing vector: Lower bound on mean interference-to-signal ratio

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https://ieeexplore.ieee.org/document/10096924

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https://doi.org/10.1109/ICASSP49357.2023.10096924
 http://hdl.handle.net/11603/28417

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UMBC Computer Science and Electrical Engineering Department
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https://orcid.org/0000-0003-0594-2796

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2023-05-05

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conference papers and proceedings
postprints
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Citation of Original Publication

J. Čmejla, Z. Koldovský, V. Kautský and T. Adali, "Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10096924.

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Abstract

This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constant Separation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as a convex combination of its initial and final values. We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cramér-Rao theory. The bound reveals advantageous properties of CvxCSV compared with CSV and compared with a sequential BSE based on independent component extraction (ICE). In particular, the achievable ISR by CvxCSV is lower than that by the previous approaches. Moreover, the model requires significantly weaker conditions for identifiability, even when the SOI is Gaussian.