Original title: Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
Authors: Šembera, Ondřej ; Tichavský, Petr ; Koldovský, Zbyněk
Document type: Research reports
Year: 2016
Language: eng
Series: Research Report, volume: 2360
Abstract: In many applications, there is a need to blindly separate independent sources from their linear instantaneous mixtures while the mixing matrix or source properties are slowly or abruptly changing in time. The easiest way to separate the data is to consider off-line estimation of the model parameters repeatedly in time shifting window. Another popular method is the stochastic natural gradient algorithm, which relies on non-Gaussianity of the separated signals and is adaptive by its nature. In this paper, we propose an adaptive version of two blind source separation algorithms which exploit non-stationarity of the original signals. The results indicate that the proposed algorithms slightly outperform the natural gradient in the trade-off between the algorithm’s ability to quickly adapt to changes in the mixing matrix and the variance of the estimate when the mixing is stationary.
Keywords: algorithms; blind separation; block gaussian separation
Project no.: FV10645
Funding provider: GA MPO

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2017/SI/tichavsky-0480768.pdf
Original record: http://hdl.handle.net/11104/0276463

Permalink: http://www.nusl.cz/ntk/nusl-369355


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