Sparse adaptive filters for echo cancellation /

Sparse adaptive filters for echo cancellation /

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Bibliographic Details
Author / Creator:Paleologu, Constantin.
Imprint:San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2010.
Description:1 electronic text (ix, 114 p. : ill.) : digital file.
Language:English
Series:Synthesis lectures on speech and audio processing, 1932-1678 ; # 6
Synthesis digital library of engineering and computer science.
Synthesis lectures on speech and audio processing, # 6.
Subject:
Adaptive filters -- Mathematical models.
Echo suppression (Telecommunication) -- Mathematical models.
Adaptive filters -- Mathematical models.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8512890
Hidden Bibliographic Details
Other authors / contributors:Benesty, Jacob.
Ciochină, Silviu.
ISBN:9781598293074 (electronic bk.)
9781598293067 (pbk.)
Notes:Title from PDF t.p. (viewed on June 4, 2010).
Series from website.
Includes bibliographical references (p. 103-109) and index.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Also available in print.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Summary: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.
Standard no.:10.2200/S00289ED1V01Y201006SAP006
Table of Contents:
  • 1. Introduction
  • Echo Cancellation
  • Double-Talk Detection
  • Sparse Adaptive Filters
  • Notation
  • Sparseness Measures
  • Vector Norms
  • Examples of Impulse Responses
  • 2. Sparseness Measure Based on the L0 Norm
  • Sparseness Measure Based on the L1 and L2 Norms
  • Sparseness Measure Based on the L1 and L[infinity] Norms
  • Sparseness Measure Based on the L2 and L[infinity] Norms
  • 3. Performance Measures
  • Mean-Square Error
  • Echo-Return Loss Enhancement
  • Misalignment
  • 4. Wiener and Basic Adaptive Filters
  • Wiener Filter
  • Efficient Computation of the Wiener-Hopf Equations
  • Deterministic Algorithm
  • Stochastic Algorithm
  • Variable Step-Size NLMS Algorithm
  • Convergence of the Misalignment
  • Sign Algorithms
  • 5. Basic Proportionate-Type NLMS Adaptive Filters
  • General Derivation
  • The Proportionate NLMS (PNLMS) and PNLMS++ Algorithms
  • The Signed Regressor PNLMS Algorithm
  • The Improved PNLMS (IPNLMS) Algorithms
  • The Regular IPNLMS
  • The IPNLMS with the L0 Norm
  • The IPNLMS with a Norm-Like Diversity Measure
  • 6. The Exponentiated Gradient Algorithms
  • Cost Function
  • The EG Algorithm for Positive Weights
  • The EG Algorithm for Positive and Negative Weights
  • Link Between NLMS and EG Algorithms
  • Link Between IPNLMS and EG Algorithms
  • 7. The Mu-Law PNLMS and Other PNLMS-Type Algorithms
  • The Mu-Law PNLMS Algorithms
  • The Sparseness-Controlled PNLMS Algorithms
  • The PNLMS Algorithm with Individual Activation Factors
  • 8. Variable Step-Size PNLMS Algorithms
  • Considerations on the Convergence of the NLMS Algorithm
  • A Variable Step-Size PNLMS Algorithm
  • 9. Proportionate Affine Projection Algorithms
  • Classical Derivation
  • A Novel Derivation
  • A Variable Step-Size Version
  • 10. Experimental Study
  • Experimental Conditions
  • IPNLMS Versus PNLMS
  • MPNLMS, SC-PNLMS, and IAF-PNLMS
  • VSS-IPNLMS
  • PAPAs
  • Bibliography
  • Index
  • Authors' Biographies.

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