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Sparse adaptive filters for echo cancellation

Author: Constantin Paleologu; Jacob Benesty; Silviu Ciochină
Publisher: [San Rafael, Calif.] : Morgan & Claypool, ©2010.
Series: Synthesis lectures on speech and audio processing, #6.
Edition/Format:   Print book : EnglishView all editions and formats
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  Read more...
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Document Type: Book
All Authors / Contributors: Constantin Paleologu; Jacob Benesty; Silviu Ciochină
ISBN: 1598293060 9781598293067
OCLC Number: 141384567
Description: ix, 114 pages : illustrations ; 24 cm.
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.
Series Title: Synthesis lectures on speech and audio processing, #6.
Responsibility: Constantin Paleologu, Jacob Benesty, Silviu Ciochină.

Abstract:

Presents the most important sparse adaptive filters developed for echo cancellation. Besides a comprehensive review of the basic proportionate-type algorithms, the book also presents some of the  Read more...

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