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Table of Contents
Acknowledgments; Contents; Abbreviations and Symbols; Abstract; 1 Introduction; 1.1 Problem Statement; 1.2 State of the Art in High-Resolution Spatial Sound Reproduction; 1.3 State of the Art in High-Resolution Spatial Sound Analysis; 1.4 State of the Art in Adaptive Filtering; 1.4.1 Frequency-Domain Adaptive Filtering; 1.4.2 Proportionate Adaptive Filtering Algorithms; 1.4.3 Model-Based Adaptive Filtering and Post-Processing; 1.4.4 Convergence Enhancement for Stereo Acoustic Echo Cancellation by a Preprocessing Stage; 1.5 Overview of This Book; References
Part ITheoretical Multichannel SystemIdentification2 Fundamentals of Adaptive Filter Theory; 2.1 Signal and System Model; 2.1.1 Standard Representation; 2.1.2 Compact Representation; 2.2 Optimal System Identification in Least-Squares Sense; 2.2.1 The Wiener
Hopf Equation; 2.2.2 Derivation of Iterative Estimation Approaches; References; 3 Spatio-Temporal Regularized Recursive Least Squares Algorithm; 3.1 Regularization from a Probabilistic Point of View; 3.2 Structured Regularization; 3.3 ellp,q-norm Constrained Adaptive Filtering; 3.4 Discussion of Special Cases
3.4.1 Multichannel Sparse Adaptive Filtering3.4.2 Efficient Computation of the Regularized Inverse; 3.5 Ill-Conditioning in Multichannel Adaptive Filtering and Sparseness Constraint; 3.6 Experiments; References; 4 Sparse Representation of Multichannel Acoustic Systems; 4.1 System Sparsity; 4.1.1 Prior Knowledge from Physics; 4.1.2 Incorporating the Prior Knowledge on Spatially Discrete Acoustic Systems; 4.1.3 Eigenspace Adaptive Filtering; 4.2 Signal Sparsity; 4.3 Source-Domain Estimation; 4.3.1 Permutation Problem; 4.4 Efficient System Identification in the Source Domain; 4.4.1 Algorithm
4.4.2 Adaptation Control4.5 Experiments; References; 5 Unique System Identification from Projections; 5.1 Generic Spatially Transformed Adaptive Filtering for Ill-Conditioned Problems; 5.2 System Eigenspace Estimation; 5.2.1 Validity of the Estimated Eigenspace; 5.2.2 Adaptation Control; 5.3 Experimental Results; 5.3.1 Performance Measures; 5.3.2 Simulation; References; Part IIPractical Aspects; 6 Geometrical Constraints; 6.1 Synthesis of Sound Fields; 6.2 Analytical Solution to the Synthesis of Sound Figures; 6.2.1 Mathematical Problem Formulation
6.2.2 Conditions for the Synthesis of Sound Figures6.3 Synthesis of Closed Zones of Quiet; 6.3.1 Approximation of the Driving Functions Based on the Kirchhoff
Helmholtz Integral; 6.3.2 Analytical Derivation of the Driving Functions; 6.4 Linear Distribution of Secondary Sources as Limiting Case of a Closed Distribution; 6.4.1 Linear Secondary Source Distributions; 6.4.2 Arrays with Convex Geometries as Linear Arrays; 6.4.3 Example of the Synthesis of Sound Figures on a Line Using Linear Arrays; 6.4.4 Sound Figures as Functions on Two-Dimensional Manifolds
Part ITheoretical Multichannel SystemIdentification2 Fundamentals of Adaptive Filter Theory; 2.1 Signal and System Model; 2.1.1 Standard Representation; 2.1.2 Compact Representation; 2.2 Optimal System Identification in Least-Squares Sense; 2.2.1 The Wiener
Hopf Equation; 2.2.2 Derivation of Iterative Estimation Approaches; References; 3 Spatio-Temporal Regularized Recursive Least Squares Algorithm; 3.1 Regularization from a Probabilistic Point of View; 3.2 Structured Regularization; 3.3 ellp,q-norm Constrained Adaptive Filtering; 3.4 Discussion of Special Cases
3.4.1 Multichannel Sparse Adaptive Filtering3.4.2 Efficient Computation of the Regularized Inverse; 3.5 Ill-Conditioning in Multichannel Adaptive Filtering and Sparseness Constraint; 3.6 Experiments; References; 4 Sparse Representation of Multichannel Acoustic Systems; 4.1 System Sparsity; 4.1.1 Prior Knowledge from Physics; 4.1.2 Incorporating the Prior Knowledge on Spatially Discrete Acoustic Systems; 4.1.3 Eigenspace Adaptive Filtering; 4.2 Signal Sparsity; 4.3 Source-Domain Estimation; 4.3.1 Permutation Problem; 4.4 Efficient System Identification in the Source Domain; 4.4.1 Algorithm
4.4.2 Adaptation Control4.5 Experiments; References; 5 Unique System Identification from Projections; 5.1 Generic Spatially Transformed Adaptive Filtering for Ill-Conditioned Problems; 5.2 System Eigenspace Estimation; 5.2.1 Validity of the Estimated Eigenspace; 5.2.2 Adaptation Control; 5.3 Experimental Results; 5.3.1 Performance Measures; 5.3.2 Simulation; References; Part IIPractical Aspects; 6 Geometrical Constraints; 6.1 Synthesis of Sound Fields; 6.2 Analytical Solution to the Synthesis of Sound Figures; 6.2.1 Mathematical Problem Formulation
6.2.2 Conditions for the Synthesis of Sound Figures6.3 Synthesis of Closed Zones of Quiet; 6.3.1 Approximation of the Driving Functions Based on the Kirchhoff
Helmholtz Integral; 6.3.2 Analytical Derivation of the Driving Functions; 6.4 Linear Distribution of Secondary Sources as Limiting Case of a Closed Distribution; 6.4.1 Linear Secondary Source Distributions; 6.4.2 Arrays with Convex Geometries as Linear Arrays; 6.4.3 Example of the Synthesis of Sound Figures on a Line Using Linear Arrays; 6.4.4 Sound Figures as Functions on Two-Dimensional Manifolds