Proportionate normalized subband adaptive filter algorithms for sparse system identification

Author:

Abadi Mohammad Shams Esfand

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software,Control and Systems Engineering

Reference16 articles.

1. Adaptive Filter Theory;Haykin,2002

2. Fundamentals of Adaptive Filtering;Sayed,2003

3. Proportionate normalized least-mean-squares adaptation in echo cancelers;Duttweiler;IEEE Transactions on Speech and Audio Processing,2000

4. S.L. Gay, An efficient, fast converging adaptive filter for network echo cancellation, in: Proceedings of the Asilomar Conference on Signals, Systems, Computer, Pacific Grove, CA, USA, November 1998, pp. 394–398.

5. J. Benesty, S.L. Gay, An improved PNLMS algorithm, in: Proceedings of the ICASSP-02, Orlando, USA, May 2002, pp. 1881–1884.

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1. Alternate Implementation of NSAF and NLMS Learning Rules for Adaptive Filters;IEEE Transactions on Circuits and Systems II: Express Briefs;2023-09

2. A Switching-Based Variable Step-Size PNLMS Adaptive Filter for Sparse System Identification;Circuits, Systems, and Signal Processing;2023-08-29

3. General Robust Proportionate NSAF Algorithm With a Step-Size Converter;IEEE Transactions on Circuits and Systems II: Express Briefs;2023-02

4. Design of a Tap-Amplitude-Based Block Proportional Adaptive Filtering Algorithm;IEEE Transactions on Signal Processing;2023

5. Coefficients-Switched Normalized Least-Mean- Squares Adaption in Echo Canceler of Sparse-Echo-Path;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2023

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