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Nonnegative Mixture for Underdetermined Blind Source Separation Based on a Tensor Algorithm

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Abstract

In this study, a tensor algorithm is proposed to blindly separate an instantaneous linear underdetermined mixture with non-stationary sources and nonnegative mixing matrix. It proceeds in two steps: 1) estimating the mixing matrix and 2) recovering the source signals. First, a canonical tensor model is constructed using a fourth-order cumulant tensor of the observed signals to estimate the mixing matrix. Then, an improved hierarchical alternating least squares algorithm is used to decompose the canonical tensor model, which ensures that all elements of the mixing matrix are positive. Finally, the sources are recovered using a minimum mean-squared error beamformer approach without any hypothetical limitation. We apply two classes of data (speech signals and biomedical signals) to substantiate the effectiveness of the proposed algorithm for underdetermined blind source separation.

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Acknowledgments

This study is supported by the National Nature Science Foundation of China (Project No. 61374154), the Fundamental Research Funds for the Central Universities (DUT13JB08), and the Dalian Municipal Science and Technology Plan Project (2012C014).

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Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116023, China

    Sunan Ge & Min Han

  2. Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian, 116044, China

    Jie Han

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  1. Sunan Ge
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  2. Jie Han
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  3. Min Han
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Correspondence to Min Han.

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Ge, S., Han, J. & Han, M. Nonnegative Mixture for Underdetermined Blind Source Separation Based on a Tensor Algorithm. Circuits Syst Signal Process 34, 2935–2950 (2015). https://doi.org/10.1007/s00034-015-9969-8

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  • DOI: https://doi.org/10.1007/s00034-015-9969-8

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