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Linear Prediction Based Blind Source Extraction Algorithms in Practical Applications

  • Conference paper
Independent Component Analysis and Signal Separation (ICA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4666))

Abstract

Blind source extraction (BSE) is of advantages over blind source separation (BSS) when obtaining some underlying source signals from high dimensional observed signals. Among a variety of BSE algorithms, a large number of algorithms are based on linear prediction (LP-BSE). In this paper we analyze them from practical point of view. We reveal that they are, in nature, minor component analysis (MCA) algorithms, and thus they have some problems that are inherent in MCA algorithms. We also find a switch phenomenon of online LP-BSE algorithms, showing that different parts of a single extracted signal are the counterparts of different source signals. The two issues should be noticed when one applies these algorithms to practical applications. Computer simulations are given to confirm these observations.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

    Zhi-Lin Zhang & Liqing Zhang

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

    Zhi-Lin Zhang

Authors
  1. Zhi-Lin Zhang
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  2. Liqing Zhang
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Editor information

Mike E. Davies Christopher J. James Samer A. Abdallah Mark D Plumbley

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© 2007 Springer-Verlag Berlin Heidelberg

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Zhang, ZL., Zhang, L. (2007). Linear Prediction Based Blind Source Extraction Algorithms in Practical Applications. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_39

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  • DOI: https://doi.org/10.1007/978-3-540-74494-8_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74493-1

  • Online ISBN: 978-3-540-74494-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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