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A joint-optimization NSAF algorithm based on the first-order Markov model

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Abstract

Recently, the normalized subband adaptive filter (NSAF) algorithm has attracted much attention for handling colored input signals. Based on the first-order Markov model of the optimal weight vector, this paper provides some insights for the convergence of the standard NSAF. Following these insights, both the step size and the regularization parameter in the NSAF are jointly optimized by minimizing the mean-square deviation. The resulting joint-optimization step size and regularization parameter algorithm achieves a good tradeoff between fast convergence rate and low steady-state error. Simulation results in the context of acoustic echo cancelation demonstrate good features of the proposed algorithm.

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Acknowledgments

The authors would like to thank Dr. Li Kan in the Computational Neuro Engineering Laboratory at the University of Florida, USA, for his help in improving the presentation of the paper. This work was partially supported by National Science Foundation of P.R. China (Grant: 61271340, 61571374 and 61433011).

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Authors and Affiliations

  1. Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle, Ministry of Education, Southwest Jiaotong University, Chengdu, 610031, China

    Yi Yu & Haiquan Zhao

  2. School of Electrical Engineering at Southwest Jiaotong University, Chengdu, 610031, China

    Yi Yu & Haiquan Zhao

Authors
  1. Yi Yu
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  2. Haiquan Zhao
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Correspondence to Haiquan Zhao.

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Yu, Y., Zhao, H. A joint-optimization NSAF algorithm based on the first-order Markov model. SIViP 11, 509–516 (2017). https://doi.org/10.1007/s11760-016-0988-0

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  • DOI: https://doi.org/10.1007/s11760-016-0988-0

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