A novel cost-effective sparsity-aware algorithm with Kalman-based gain for the identification of long acoustic impulse responses

A Correction to this article was published on 18 August 2020

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Abstract

In this paper, a new robust sparsity (or sparseness)-aware adaptive filtering algorithm is proposed for the purpose of system identification and acoustic echo cancelation. It is named the improved proportionate fast normalized least mean square (IPFNLMS) algorithm. This latter has been derived by an effective integration of the update control matrix of the improved proportionate NLMS (IPNLMS) algorithm to the Kalman-based adaptation gain of the fast-NLMS (FNLMS) algorithm. Simulations were carried out both in synthetic and real long acoustic impulse responses at different sparseness levels with stationary and non-stationary inputs, followed by a verification with real experiment data. Results have shown interesting improvements for the proposed algorithm with respect to its ancestors in terms of convergence speed, steady-state performance, tracking capability and robustness against system sparsity variation.

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Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their thoughtful comments and constructive suggestions that helped to improve the quality of this paper.

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Correspondence to Ayoub Tedjani.

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Tedjani, A., Benallal, A. A novel cost-effective sparsity-aware algorithm with Kalman-based gain for the identification of long acoustic impulse responses. SIViP 14, 1679–1687 (2020). https://doi.org/10.1007/s11760-020-01715-2

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Keywords

  • Adaptive filtering
  • Sparse algorithms
  • System identification
  • Acoustic echo cancelation
  • FNLMS
  • Convergence speed
  • Tracking capability