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Blind Source Separation of Single Channel Mixture Using Tensorization and Tensor Diagonalization

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)

Abstract

This paper deals with estimation of structured signals such as damped sinusoids, exponentials, polynomials, and their products from single channel data. It is shown that building tensors from this kind of data results in tensors with hidden block structure which can be recovered through the tensor diagonalization. The tensor diagonalization means multiplying tensors by several matrices along its modes so that the outcome is approximately diagonal or block-diagonal of 3-rd order tensors. The proposed method can be applied to estimation of parameters of multiple damped sinusoids, and their products with polynomial.

Keywords

Blind source separation Block tensor decomposition Tensor diagonalization Three-way folding Three-way toeplitzation Damped sinusoids 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Lab for Advanced Brain Signal ProcessingBrain Science Institute - RIKENWakoJapan
  2. 2.Institute of Information Theory and AutomationPragueCzech Republic
  3. 3.Skolkovo Institute of Science and Technology (SKOLTECH)MoscowRussia

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