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Underdetermined mixing matrix estimation based on time-frequency single source points detection and eigenvalue decomposition

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Abstract

In this paper, a method of mixing matrix estimation based on time-frequency single source points detection and eigenvalue decomposition is proposed under the underdetermined blind source separation model. Firstly, short-time Fourier transform is used to transform non-sparse observed signals in time domain to sparse signals in time-frequency domain, and SSPs detection is employed to improve the sparsity. Secondly, the improved fuzzy C-means clustering algorithm is applied to estimate the number of sources. Then, we reselect the SSPs by the means of ordinary least squares to improve the estimation accuracy. Finally, the estimated mixing matrix is obtained by eigenvalue decomposition. Vast simulations illustrate that our method can estimate the mixing matrix accurately when the number of sources is unknown, and has strong robustness to noise.

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Authors and Affiliations

  1. College of Electrical Engineering, Sichuan University, Chengdu, 610065, China

    Pengcheng Bai & Qin Shu

  2. Southwest Institute of Technical Physics, Chengdu, 610065, China

    Yunxiu Yang

  3. Department of Electronics and Information Technology, National University of Defense Technology, Changsha, 410003, China

    Fengtao Xue

  4. Beijing General Research Institute of Electronic Engineering, Beijing, 100854, China

    Rong Yang

Authors
  1. Pengcheng Bai
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  2. Yunxiu Yang
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  3. Fengtao Xue
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  4. Rong Yang
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  5. Qin Shu
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Correspondence to Qin Shu.

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Bai, P., Yang, Y., Xue, F. et al. Underdetermined mixing matrix estimation based on time-frequency single source points detection and eigenvalue decomposition. SIViP 16, 1061–1069 (2022). https://doi.org/10.1007/s11760-021-02055-5

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  • DOI: https://doi.org/10.1007/s11760-021-02055-5

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