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Sparse Adaptive Filters for Echo Cancellation

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TLDR
This book presents the most important sparse adaptive filters developed for echo cancellation and proposes some new solutions for further performance improvement, e.g., variable step-size versions and novel proportionate-type affine projection algorithms.
Abstract
Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellation. Besides a comprehensive review of the basic proportionate-type algorithms, we also present some of the latest developments in the field and propose some new solutions for further performance improvement, e.g., variable step-size versions and novel proportionate-type affine projection algorithms. An experimental study is also provided in order to compare many sparse adaptive filters in different echo canc...

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Citations
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Journal ArticleDOI

An Efficient Proportionate Affine Projection Algorithm for Echo Cancellation

TL;DR: Simulation results indicate that the proposed algorithm outperforms the classical one (achieving faster tracking and lower misadjustment) and has a lower computational complexity due to a recursive implementation of the ¿proportionate history¿.
Journal ArticleDOI

Combinations of Adaptive Filters: Performance and convergence properties

TL;DR: Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation to array beamforming.
Journal ArticleDOI

On Regularization in Adaptive Filtering

TL;DR: This paper proposes one possible way to regularize an adaptive filter based on a condition that intuitively makes sense and shows how to regularized four important algorithms: the normalized least-mean-square (NLMS), the signed-regressor NLMS (SR-NLMS, the improved proportionate NLMS, and the SR-IPNLMS.
Journal ArticleDOI

Study of the General Kalman Filter for Echo Cancellation

TL;DR: This work derives a different form of the Kalman filter by considering, at each iteration, a block of time samples instead of one time sample as it is the case in the conventional approach.
Journal ArticleDOI

Linear System Identification Based on a Kronecker Product Decomposition

TL;DR: A new way to address the system identification problem (from the echo cancelation perspective) is proposed, by exploiting an optimal approximation of the impulse response based on the nearest Kronecker product decomposition and developing an iterative Wiener filter based on this approach.
References
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