Microphone Array Signal Processing

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Springer Science & Business Media, Mar 11, 2008 - Technology & Engineering - 240 pages
1 Review
In the past few years we have written and edited several books in the area of acousticandspeechsignalprocessing. Thereasonbehindthisendeavoristhat there were almost no books available in the literature when we ?rst started while there was (and still is) a real need to publish manuscripts summarizing the most useful ideas, concepts, results, and state-of-the-art algorithms in this important area of research. According to all the feedback we have received so far, we can say that we were right in doing this. Recently, several other researchers have followed us in this journey and have published interesting books with their own visions and perspectives. The idea of writing a book on Microphone Array Signal Processing comes from discussions we have had with many colleagues and friends. As a c- sequence of these discussions, we came up with the conclusion that, again, there is an urgent need for a monograph that carefully explains the theory and implementation of microphone arrays. While there are many manuscripts on antenna arrays from a narrowband perspective (narrowband signals and narrowband processing), the literature is quite scarce when it comes to s- sor arrays explained from a truly broadband perspective. Many algorithms for speech applications were simply borrowed from narrowband antenna - rays. However, a direct application of narrowband ideas to broadband speech processing may not be necessarily appropriate and can lead to many m- understandings.
 

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Contents

Introduction
1
12 Organization of the Book
5
Classical Optimal Filtering
7
22 Wiener Filter
8
232 Generalized Sidelobe Canceller Structure
17
233 Application to Linear Interpolation
19
24 Kalman Filter
21
25 A Viable Alternative to the MSE
25
664 Spatial Maximum SNR Filter
132
665 Minimum Variance Distortionless Response Filter
134
666 Distortionless Multichannel Wiener Filter
135
67 Conclusions
136
Microphone Arrays from a MIMO Perspective
138
72 Signal Models and Problem Description
140
721 SISO Model
141
723 MISO Model
142

252 Important Relations with the SPCC
26
253 Examples of Optimal Filters Derived from the SPCC
29
26 Conclusions
37
Conventional Beamforming Techniques
39
32 Problem Description
40
33 DelayandSum Technique
41
34 Design of a Fixed Beamformer
46
35 Maximum SignaltoNoise Ratio Filter
49
36 Minimum Variance Distortionless Response Filter
52
37 Approach with a Reference Signal
54
38 ResponseInvariant Broadband Beamformers
55
39 NullSteering Technique
58
3101 First Signal Model
62
3102 Second Signal Model
64
311 Conclusions
65
On the Use of the LCMV Filter in Room Acoustic Environments
66
421 Anechoic Model
68
423 SpatioTemporal Model
69
44 The LCMV Filter with the Reverberant Model
73
45 The LCMV Filter with the SpatioTemporal Model
75
451 Experimental Results
78
46 The LCMV Filter in the Frequency Domain
81
47 Conclusions
83
Noise Reduction with Multiple Microphones a Unified Treatment
85
52 Signal Model and Problem Description
86
53 Some Useful Definitions
87
54 Wiener Filter
89
55 Subspace Method
92
56 SpatioTemporal Prediction Approach
95
57 Case of Perfectly Coherent Noise
97
58 Adaptive Noise Cancellation
99
59 Kalman Filter
100
510 Simulations
101
5102 Experimental Results
103
511 Conclusions
114
Noncausal FrequencyDomain Optimal Filters
115
62 Signal Model and Problem Formulation
116
63 Performance Measures
117
64 Noncausal Wiener Filter
120
65 Parametric Wiener Filtering
124
66 Generalization to the Multichannel Case
126
662 Definitions
128
663 Multichannel Wiener Filter
129
724 MIMO Model
143
725 Problem Description
144
731 LeastSquares Approach
145
732 Frost Algorithm
146
733 Generalized Sidelobe Canceller Structure
148
74 NElement Microphone Array
150
742 Frost Algorithm
152
743 Generalized Sidelobe Canceller Structure
154
744 Minimum Variance Distortionless Response Approach
156
76 Conclusions
163
Sequential Separation and Dereverberation the TwoStage Approach
165
83 Source Separation
168
832 M X N MIMO System
172
84 Speech Dereverberation
175
842 Minimum MeanSquare Error and LeastSquares Methods
177
85 Conclusions
180
DirectionofArrival and TimeDifferenceofArrival Estimation
181
92 Problem Formulation and Signal Models
184
922 MultipleSource FreeField Model
185
923 SingleSource Reverberant Model
186
924 MultipleSource Reverberant Model
187
93 CrossCorrelation Method
188
94 The Family of the Generalized CrossCorrelation Methods
190
941 Classical CrossCorrelation
191
943 Phase Transform
192
95 Spatial Linear Prediction Method
193
96 Multichannel CrossCorrelation Coefficient Algorithm
196
97 EigenvectorBased Techniques
200
971 Narrowband MUSIC
201
972 Broadband MUSIC
203
98 Minimum Entropy Method
205
982 Speech Source Signal
206
99 Adaptive Eigenvalue Decomposition Algorithm
207
910 Adaptive Blind Multichannel Identification Based Methods
209
911 TDOA Estimation of Multiple Sources
211
912 Conclusions
215
Unaddressed Problems
216
103 Cocktail Party Effect and Blind Source Separation
218
104 Blind MIMO Identification
220
105 Conclusions
222
References
223
Index
237
Copyright

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Page 234 - HL Van Trees, Optimum Array Processing. Part IV of Detection, Estimation, and Modulation Theory.
Page 227 - Performance analysis of a nullsteering algorithm based on direction-of-arrival estimation,
Page 223 - Adaptive arrays," IEEE Trans. Antennas Propagat., vol. AP-24, pp. 585-598, 1976.
Page 228 - A. Graham, Kronecker Products and Matrix Calculus with Applications. New York: John Wiley and Sons. 1981. 1 7. TG Stockham, Jr., "High-Speed Convolution and Correlation," in Spring Joint Computer Conf, AF1PS Proc., vol.
Page 223 - Space-time processing for optimal parameter estimation," in Signal Processing (J. Griffiths, P. Stocklin, and CV Schooneveld, eds.), pp.

About the author (2008)

INRS-EMT, University of Quebec