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 cancellation scenarios. Table of Contents: Introduction / Sparseness Measures / Performance Measures / Wiener and Basic Adaptive Filters / Basic Proportionate-Type NLMS Adaptive Filters / The Exponentiated Gradient Algorithms / The Mu-Law PNLMS and Other PNLMS-Type Algorithms / Variable Step-Size PNLMS Algorithms / Proportionate Affine Projection Algorithms / Experimental Study

具有大量系数的自适应滤波器通常涉及网络和声学回声消除。因此，改善用于这些应用的传统算法的收敛率和跟踪是很重要的。这可以通过利用回声路径的稀疏性来实现。稀疏脉冲响应的识别问题主要是在过去十年中随着所谓的 "比例 "型算法的发展而得到解决。本书的目标是介绍为回声消除开发的最重要的稀疏自适应滤波器。除了全面回顾基本的比例型算法外，我们还介绍了该领域的一些最新发展，并提出了一些进一步提高性能的新方案，如可变步长版本和新型比例型仿射算法。我们还提供了一个实验研究，以便在不同的回声消除情况下比较许多稀疏自适应滤波器。目录。引言/稀疏度测量/性能测量/维纳和基本自适应滤波器/基本比例型NLMS自适应滤波器/指数梯度算法/Mu-Law PNLMS和其他PNLMS型算法/可变步长PNLMS算法/比例型仿射算法/实验研究