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Blind Source Extraction for Noisy Mixtures by Combining Gaussian Moments and Generalized Autocorrelations

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Abstract

In the blind source extraction problem, the concept of generalized autocorrelations has been successfully used when the desired signal has special temporal structures. However, their applications are only limited to noise-free mixtures, which is not realistic. Therefore, this paper addresses the extraction of the noisy model based on these temporal characteristics of sources. An objective function, which combines Gaussian moments and generalized autocorrelations, is proposed. Maximizing this objective function, we present a blind source extraction algorithm for noisy mixtures. Simulations on synthesized signals, images, artificial electrocardiogram (ECG) data and the real-world ECG data show the better performance of the proposed algorithm. Moreover, comparisons with the existing algorithms further indicate its validity and also show its robustness to the estimated error of time delay.

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Authors and Affiliations

  1. Department of Applied Mathematics, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Hongjuan Zhang & Enmin Feng

  2. Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing, 100083, People’s Republic of China

    Zhenwei Shi

  3. Institute of Systems Engineering, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Chonghui Guo

Authors
  1. Hongjuan Zhang
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  2. Zhenwei Shi
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  3. Chonghui Guo
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  4. Enmin Feng
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Correspondence to Chonghui Guo.

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Zhang, H., Shi, Z., Guo, C. et al. Blind Source Extraction for Noisy Mixtures by Combining Gaussian Moments and Generalized Autocorrelations. Neural Process Lett 28, 209–225 (2008). https://doi.org/10.1007/s11063-008-9091-z

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  • DOI: https://doi.org/10.1007/s11063-008-9091-z

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