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Perspectives in Mathematical System Theory, Control, and Signal Processing pp 321–330Cite as

Sparse Blind Source Separation via ℓ1-Norm Optimization

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Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 398))

Abstract

The title of the paper refers to an extension of the classical blind source separation where the mixing of unknown sources is assumed in the form of convolution with impulse response of unknown linear dynamics. A further key assumption of our approach is that source signals are considered to be sparse with respect to a known dictionary, and thereby, an ℓ1-optimization is a natural formalism for solving the un-mixing problem.We demonstrate the effectiveness of the framework numerically.

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Author information

Authors and Affiliations

  1. University of Minnesota, Minneapolis, MN, USA

    Tryphon T. Georgiou

  2. Georgia Institute of Technology, Atlanta, GA, USA

    Allen Tannenbaum

  3. Technion, Haifa, Israel

    Allen Tannenbaum

Authors
  1. Tryphon T. Georgiou
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  2. Allen Tannenbaum
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Editor information

Editors and Affiliations

  1. ESAT, K.U.Leuven, Kasteelpark Arenberg 10, 3001, Leuven, Belgium

    Jan C. Willems

  2. Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656, Tokyo, Japan

    Shinji Hara

  3. Control Systems Theory Group, Department of Applied Mathematics and Physics , Graduate School of Informatics, Kyoto University, Yoshida-honmachi, 606-8501, Kyoto, Japan

    Yoshito Ohta

  4. Dept. Applied Analysis & Complex Dynamic Systems, Kyoto University, 606-8501, Kyoto, Japan

    Hisaya Fujioka

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Georgiou, T.T., Tannenbaum, A. (2010). Sparse Blind Source Separation via ℓ1-Norm Optimization. In: Willems, J.C., Hara, S., Ohta, Y., Fujioka, H. (eds) Perspectives in Mathematical System Theory, Control, and Signal Processing. Lecture Notes in Control and Information Sciences, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93918-4_29

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  • DOI: https://doi.org/10.1007/978-3-540-93918-4_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-93917-7

  • Online ISBN: 978-3-540-93918-4

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