A new adaptive algorithm for stereophonic acoustic echo canceller
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Approximated affine projection algorithm for feedback cancellation in hearing aids
Computer Methods and Programs in Biomedicine
A variable step-size affine projection algorithm
Digital Signal Processing
A variable regularization method for affine projection algorithm
IEEE Transactions on Circuits and Systems II: Express Briefs
A unified approach to the steady-state and tracking analyses ofadaptive filters
IEEE Transactions on Signal Processing
Mean-square performance of a family of affine projection algorithms
IEEE Transactions on Signal Processing
A Variable Step-Size Affine Projection Algorithm Designed for Acoustic Echo Cancellation
IEEE Transactions on Audio, Speech, and Language Processing
On Regularization in Adaptive Filtering
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, we propose a variable matrix-type step-size affine projection algorithm (APA) with orthogonalized input vectors. We generate orthogonalized input vectors using the Gram-Schmidt process to implement the weight update equation of the APA using the sum of normalized least mean squares (NLMS)-like updating equations. This method allows us to use individual step sizes corresponding to each NLMS-like equation, which is equivalent to adopting the step size in the form of a diagonal matrix in the APA. We adopt a variable step-size scheme, in which the individual step sizes are determined to minimize the mean square deviation of the APA in order to achieve the fastest convergence on every iteration. Furthermore, because of the weight vector updated successively only along each innovative one among the reused inputs and effect of the regularization absorbed into the derived step size, the algorithm works well even for badly excited input signals. Experimental results show that our proposed algorithm has almost optimal performance in terms of convergence rate and steady-state estimation error, and these results are remarkable especially for badly excited input signals.