The Resource Adaptive identification of acoustic multichannel systems using sparse representations, Karim Helwani

Adaptive identification of acoustic multichannel systems using sparse representations, Karim Helwani

Label
Adaptive identification of acoustic multichannel systems using sparse representations
Title
Adaptive identification of acoustic multichannel systems using sparse representations
Statement of responsibility
Karim Helwani
Creator
Author
Subject
Genre
Language
eng
Summary
This book treats the topic of extending the adaptive filtering theory in the context of massive multichannel systems by taking into account a priori knowledge of the underlying system or signal. The starting point is exploiting the sparseness in acoustic multichannel system in order to solve the non-uniqueness problem with an efficient algorithm for adaptive filtering that does not require any modification of the loudspeaker signals. The book discusses in detail the?derivation of general sparse representations of acoustic MIMO systems?in signal or system dependent transform domains.?Efficient a
Member of
Cataloging source
GW5XE
http://library.link/vocab/creatorName
Helwani, Karim
Dewey number
621.382/2
Illustrations
illustrations
Index
no index present
LC call number
TK5102.9
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Series statement
T-Labs Series in Telecommunication Services,
http://library.link/vocab/subjectName
  • Adaptive signal processing
  • MIMO systems
  • TECHNOLOGY & ENGINEERING
  • Adaptive signal processing
  • MIMO systems
Label
Adaptive identification of acoustic multichannel systems using sparse representations, Karim Helwani
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-08954-6
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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
Dimensions
unknown
Extent
1 online resource (xiv, 113 pages)
File format
unknown
Form of item
online
Isbn
9783319089546
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other control number
10.1007/978-3-319-08954-6
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)885304255
  • (OCoLC)ocn885304255
Label
Adaptive identification of acoustic multichannel systems using sparse representations, Karim Helwani
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-08954-6
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
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
Dimensions
unknown
Extent
1 online resource (xiv, 113 pages)
File format
unknown
Form of item
online
Isbn
9783319089546
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other control number
10.1007/978-3-319-08954-6
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)885304255
  • (OCoLC)ocn885304255

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