Adaptive signal processing
Adaptive filter theory
Step-size control for acoustic echo cancellation filter—an overview
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive algorithms for sparse impulse response identification
Adaptive algorithms for sparse impulse response identification
Acoustic MIMO Signal Processing (Signals and Communication Technology)
Acoustic MIMO Signal Processing (Signals and Communication Technology)
Set-membership proportionate affine projection algorithms
EURASIP Journal on Audio, Speech, and Music Processing
A low delay and fast converging improved proportionate algorithm for sparse system identification
EURASIP Journal on Audio, Speech, and Music Processing
Exploiting sparsity in adaptive filters
IEEE Transactions on Signal Processing
Proportionate adaptive algorithms for network echo cancellation
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
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Proportionate adaptive algorithms have been proposed recently to accelerate convergence for the identification of sparse impulse response. When the excitation signal is colored, especially the speech, the convergence performance of proportionate NLMS algorithms demonstrate slow convergence speed. The proportionate affine projection algorithm (PAPA) is expected to solve this problem by using more information in the input signals. However, its steady-state performance is limited by the constant step-size parameter. In this article we propose a variable step-size PAPA by canceling the a posteriori estimation error. This can result in high convergence speed using a large step size when the identification error is large, and can then considerably decrease the steady-state misalignment using a small step size after the adaptive filter has converged. Simulation results show that the proposed approach can greatly improve the steady-state misalignment without sacrificing the fast convergence of PAPA.