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Microphone Acousitc Array Sys
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Artikel-Nr:
9780470827239
Veröffentl:
2013
Erscheinungsdatum:
30.04.2013
Seiten:
536
Autor:
Mingsian R Bai
Gewicht:
923 g
Format:
235x157x33 mm
Sprache:
Englisch
Beschreibung:
Mingsian R. Bai, National Tsing Hua University, Taiwan
Jeong-Guon Ih, Korea Advanced Institute of Science and Technology (KAIST), South Korea
Jacob Benesty, University of Quebec, Canada
Jeong-Guon Ih, Korea Advanced Institute of Science and Technology (KAIST), South Korea
Jacob Benesty, University of Quebec, Canada
Presents a unified framework of far-field and near-field array techniques for noise source identification and sound field visualization, from theory to application.
Acoustic Array Systems: Theory, Implementation, and Application provides an overview of microphone array technology with applications in noise source identification and sound field visualization. In the comprehensive treatment of microphone arrays, the topics covered include an introduction to the theory, far-field and near-field array signal processing algorithms, practical implementations, and common applications: vehicles, computing and communications equipment, compressors, fans, and household appliances, and hands-free speech. The author concludes with other emerging techniques and innovative algorithms.
* Encompasses theoretical background, implementation considerations and application know-how
* Shows how to tackle broader problems in signal processing, control, and transudcers
* Covers both farfield and nearfield techniques in a balanced way
* Introduces innovative algorithms including equivalent source imaging (NESI) and high-resolution nearfield arrays
* Selected code examples available for download for readers to practice on their own
* Presentation slides available for instructor use
A valuable resource for Postgraduates and researchers in acoustics, noise control engineering, audio engineering, and signal processing.
Acoustic Array Systems: Theory, Implementation, and Application provides an overview of microphone array technology with applications in noise source identification and sound field visualization. In the comprehensive treatment of microphone arrays, the topics covered include an introduction to the theory, far-field and near-field array signal processing algorithms, practical implementations, and common applications: vehicles, computing and communications equipment, compressors, fans, and household appliances, and hands-free speech. The author concludes with other emerging techniques and innovative algorithms.
* Encompasses theoretical background, implementation considerations and application know-how
* Shows how to tackle broader problems in signal processing, control, and transudcers
* Covers both farfield and nearfield techniques in a balanced way
* Introduces innovative algorithms including equivalent source imaging (NESI) and high-resolution nearfield arrays
* Selected code examples available for download for readers to practice on their own
* Presentation slides available for instructor use
A valuable resource for Postgraduates and researchers in acoustics, noise control engineering, audio engineering, and signal processing.
Preface xi
Acknowledgments xiii
Glossary: Symbols and Abbreviations xv
1 Introduction 1
1.1 Background and Motivation 1
1.2 Review of Prior Approaches for Noise Identification Problems 3
1.3 Organization of the Book 4
References 5
2 Theoretical Preliminaries of Acoustics 9
2.1 Fundamentals of Acoustics 9
2.2 Sound Field Representation Using Basis Function Expansion 16
2.3 Sound Field Representation Using Helmholtz Integral Equation 19
2.4 Inverse Problems and Ill-Posedness 31
References 32
3 Theoretical Preliminaries of Array Signal Processing 33
3.1 Linear Algebra Basics 33
3.2 Digital Signal Processing Basics 42
3.3 Array Signal Processing Basics 64
3.4 Optimization Algorithms 77
3.5 Inverse Filtering from a Model Matching Perspective 85
3.6 Parameter Estimation Theory 88
3.6.1 Classical Approaches 89
3.6.2 Bayesian Approaches 90
References 93
4 Farfield Array Signal Processing Algorithms 95
4.1 Low-Resolution Algorithms 96
4.1.1 Fourier Beamformer 96
4.1.2 Time Reversal Beamformer 99
4.1.3 SIMO-ESIF Algorithm 100
4.1.4 Choice of Farfield Array Parameters 102
4.2 High-Resolution Algorithms 102
4.2.1 Minimum Variance Beamformers 103
4.2.2 Optimal Arrays 108
4.2.3 DMA Versus GSC 130
4.2.4 Auto-Regressive Array Design 136
4.2.5 Multiple Signal Classification (MUSIC) 140
4.2.6 Choice of Parameters in MUSIC 144
4.3 Comparison of the Farfield Algorithms 145
References 150
5 Nearfield Array Signal Processing Algorithms 151
5.1 Fourier NAH 151
5.2 Basis Function Model (BFM)-based NAH 155
5.2.1 Spherical Waves 158
5.2.2 HELS Method: A Single-Point Multipole Method 160
5.3 BEM-based NAH (IBEM): Direct and Indirect Formulations 163
5.3.1 Direct IBEM Formulation 163
5.3.2 Indirect IBEM Formulation 168
5.3.3 Detailed Exposition of the Direct BEM-based NAH 169
5.4 Equivalent Source Model (ESM)-based NAH 177
5.4.1 Indirect ESM 178
5.4.2 ESM Combined with BEM-based NAH 181
5.4.3 Direct ESM 191
5.4.4 Nearfield Equivalent Source Imaging (NESI) 195
5.4.5 Kalman Filter-based Algorithm 196
5.4.6 Choice of Nearfield Array Parameters 204
5.5 Comparison of the Nearfield Algorithms 205
References 208
6 Practical Implementation 211
6.1 Inverse Filter Design 211
6.1.1 Model Matching: Ill-Posedness and Regularization 211
6.1.2 Window Design 213
6.1.3 Parameter Choice Methods (PCM) 214
6.2 Multi-Channel Fast Filtering 216
6.2.1 The Time-Domain Processing 218
6.2.2 The Frequency-Domain Processing 218
6.2.3 Comparison of Filtering Approaches 220
6.3 Post-Processing 221
6.3.1 Acoustic Variables 221
6.3.2 Processing of Moving Sources 223
6.4 Choice of Distance of Reconstruction and Lattice Spacing 226
6.5 Virtual Microphone Technique: Field Interpolation and Extrapolation 227
6.5.1 Sound Field Interpolation by ESM 227
6.5.2 More Resolution-Enhancing Reconstruction Strategies 229
6.6 Choice of Retreat Distance 234
6.6.1 Integral Approximation Error vs. Reconstruction Ill-Posedness 234
6.6.2 Determination of RD: Golden Section Search 235
6.7 Optimization of Sensor Deployment: Uniform vs
Acknowledgments xiii
Glossary: Symbols and Abbreviations xv
1 Introduction 1
1.1 Background and Motivation 1
1.2 Review of Prior Approaches for Noise Identification Problems 3
1.3 Organization of the Book 4
References 5
2 Theoretical Preliminaries of Acoustics 9
2.1 Fundamentals of Acoustics 9
2.2 Sound Field Representation Using Basis Function Expansion 16
2.3 Sound Field Representation Using Helmholtz Integral Equation 19
2.4 Inverse Problems and Ill-Posedness 31
References 32
3 Theoretical Preliminaries of Array Signal Processing 33
3.1 Linear Algebra Basics 33
3.2 Digital Signal Processing Basics 42
3.3 Array Signal Processing Basics 64
3.4 Optimization Algorithms 77
3.5 Inverse Filtering from a Model Matching Perspective 85
3.6 Parameter Estimation Theory 88
3.6.1 Classical Approaches 89
3.6.2 Bayesian Approaches 90
References 93
4 Farfield Array Signal Processing Algorithms 95
4.1 Low-Resolution Algorithms 96
4.1.1 Fourier Beamformer 96
4.1.2 Time Reversal Beamformer 99
4.1.3 SIMO-ESIF Algorithm 100
4.1.4 Choice of Farfield Array Parameters 102
4.2 High-Resolution Algorithms 102
4.2.1 Minimum Variance Beamformers 103
4.2.2 Optimal Arrays 108
4.2.3 DMA Versus GSC 130
4.2.4 Auto-Regressive Array Design 136
4.2.5 Multiple Signal Classification (MUSIC) 140
4.2.6 Choice of Parameters in MUSIC 144
4.3 Comparison of the Farfield Algorithms 145
References 150
5 Nearfield Array Signal Processing Algorithms 151
5.1 Fourier NAH 151
5.2 Basis Function Model (BFM)-based NAH 155
5.2.1 Spherical Waves 158
5.2.2 HELS Method: A Single-Point Multipole Method 160
5.3 BEM-based NAH (IBEM): Direct and Indirect Formulations 163
5.3.1 Direct IBEM Formulation 163
5.3.2 Indirect IBEM Formulation 168
5.3.3 Detailed Exposition of the Direct BEM-based NAH 169
5.4 Equivalent Source Model (ESM)-based NAH 177
5.4.1 Indirect ESM 178
5.4.2 ESM Combined with BEM-based NAH 181
5.4.3 Direct ESM 191
5.4.4 Nearfield Equivalent Source Imaging (NESI) 195
5.4.5 Kalman Filter-based Algorithm 196
5.4.6 Choice of Nearfield Array Parameters 204
5.5 Comparison of the Nearfield Algorithms 205
References 208
6 Practical Implementation 211
6.1 Inverse Filter Design 211
6.1.1 Model Matching: Ill-Posedness and Regularization 211
6.1.2 Window Design 213
6.1.3 Parameter Choice Methods (PCM) 214
6.2 Multi-Channel Fast Filtering 216
6.2.1 The Time-Domain Processing 218
6.2.2 The Frequency-Domain Processing 218
6.2.3 Comparison of Filtering Approaches 220
6.3 Post-Processing 221
6.3.1 Acoustic Variables 221
6.3.2 Processing of Moving Sources 223
6.4 Choice of Distance of Reconstruction and Lattice Spacing 226
6.5 Virtual Microphone Technique: Field Interpolation and Extrapolation 227
6.5.1 Sound Field Interpolation by ESM 227
6.5.2 More Resolution-Enhancing Reconstruction Strategies 229
6.6 Choice of Retreat Distance 234
6.6.1 Integral Approximation Error vs. Reconstruction Ill-Posedness 234
6.6.2 Determination of RD: Golden Section Search 235
6.7 Optimization of Sensor Deployment: Uniform vs