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A robust algorithm for convolutive blind source separation in presence of noise

机译:噪声存在下卷积盲源分离的鲁棒算法

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摘要

We consider the blind source separation (BSS) problem in the noisy context. We propose a new methodology in order to enhance separation performances in terms of efficiency and robustness. Our approach consists in denoising the observed signals through the minimization of their total variation, and then minimizing divergence separation criteria combined with the total variation of the estimated source signals. We show by the way that the method leads to some projection problems that are solved by means of projected gradient algorithms. The efficiency and robustness of the proposed algorithm using Hellinger divergence are illustrated and compared with the classical mutual information approach through numerical simulations.
机译:我们在嘈杂的环境中考虑盲源分离(BSS)问题。我们提出了一种新的方法,以提高分离效率的效率和稳定性。我们的方法包括通过最小化观测信号的总变化量对信号进行去噪,然后最小化发散分离标准以及估计源信号的总变化量。通过方式说明,该方法导致一些投影问题,这些问题可以通过投影梯度算法解决。通过数值模拟,说明了该算法的效率和鲁棒性,并采用经典的互信息方法进行了比较。

著录项

  • 来源
    《Signal processing》 |2013年第4期|818-827|共10页
  • 作者单位

    LAMAI, FSTG. Universite Cadi Ayyad-Marrakech, Morocco;

    CReSTIC, Universite de Reims Champagne-Ardenne, France;

    Laboratoire de Mathematiques de Reims EA 4535, Universite de Reims Champagne-Ardenne, France and Federation ARC Mathematiques FR 3399 du CNRS, France;

    Aix Marseille Universite, CNRS, ENSAM, LS1S, UMR 7296, 13397 Marseille, France, and Universite de Toulon, CNRS. LSIS, UMR 7296, 83957 La Garde, France;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    blind source separation; noisy convolutive mixtures; total variation; divergences;

    机译:盲源分离嘈杂的卷积混合物;总变化分歧;

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