Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment

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Springer Science & Business Media, Mar 30, 2009 - Technology & Engineering - 225 pages
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The development of computer and telecommunication technologies led to a revolutioninthewaythatpeopleworkandcommunicatewitheachother.One of the results is that large amount of information will increasingly be held in a form that is natural for users, as speech in natural language. In the presented work, we investigate the speech signal capture problem, which includes the separation of multiple interfering speakers using microphone arrays. Adaptive beamforming is a classical approach which has been developed since the seventies. However it requires a double-talk detector (DTD) that interrupts the adaptation when the target is active, since otherwise target cancelation occurs. The fact that several speakers may be active simulta- ouslymakesthisdetectiondi?cult,andifadditionalbackgroundnoiseoccurs, even less reliable. Our proposed approaches address this separation problem using continuous, uninterrupted adaptive algorithms. The advantage seems twofold:Firstly,thealgorithmdevelopmentismuchsimplersincenodetection mechanism needs to be designed and no threshold is to be tuned. Secondly, the performance may be improved due to the adaptation during periods of double-talk. In the ?rst part of the book, we investigate a modi?cation of the widely usedNLMSalgorithm,termedImplicitLMS(ILMS),whichimplicitlyincludes an adaptation control and does not require any threshold. Experimental ev- uations reveal that ILMS mitigates the target signal cancelation substantially with the distributed microphone array. However, in the more di?cult case of the compact microphone array, this algorithm does not su?ciently reduce the target signal cancelation. In this case, more sophisticated blind source se- ration techniques (BSS) seem necessary.
 

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Contents

Introduction
1
A Brief Overview
2
12 Scope and Objective of the Book
3
13 Outline of the Book
4
Source Separation as a Multichannel Linear Filtering Problem
6
22 The Separation Filters
10
222 Multiple Output Systems
13
223 The Spatial Response
14
63 Experimental Results
100
631 Experiments with the FourElement Compact Array Mounted in the RearView Mirror
101
632 Experiments with the TwoElement Distributed Array Mounted on the Car Ceiling
106
633 Comparison with Other BSS Algorithms in the Frequency Domain
108
64 Summary and Conclusion
111
On the Convergence and Stability in SecondOrder Statistics BSS
113
711 Difficulty of a Global Convergence Analysis
114
712 Convergence Analysis for a Simplified Algorithm
116

224 Particular Cases
17
23 Spatial Filtering vs Spectral Filtering
18
231 Minimum Filter Length for Spatial Separation
20
232 Particular Cases
21
24 Performance Measures
22
241 Compact Microphone Array
23
243 StartUp Performance and Performance after Initial Convergence
24
Linearly Constrained Minimum Variance Beamforming
27
32 From LCMV to Generalized Sidelobe Canceler GSC
30
33 Constraints for Compact and Distributed Setups
31
332 Constraint for Distributed Microphone Array
33
34 The Target Signal Cancelation Problem
34
341 The EnergyInversion Effect
35
On the Necessity of a DoubleTalk Detector
36
35 Summary and Conclusion
37
Implicit Adaptation Control for Beamforming
39
42 Implicit Adaptation Control
42
43 Analysis of the ILMS Algorithm
43
432 ILMS Transient Behavior and Stability
46
Transient Convergence and Divergence
47
44 Robustness Improvement
51
45 Experiments
52
451 Experiments with the FourElement Compact Array Mounted in the RearView Mirror
55
452 Experiment with the TwoElement Distributed Array Mounted on the Car Ceiling
59
46 Summary and Conclusion
61
SecondOrder Statistics Blind Source Separation
63
51 Problem and Notations
65
512 Separation Ambiguities
68
52 Nonstationarity and Source Separation
69
522 NonstationarityBased Cost Function
70
53 GradientBased Minimization
73
532 Natural Gradient
74
54 Natural Gradient Algorithm for NonSquare Systems
75
55 Summary and Conclusion
79
Implementation Issues in Blind Source Separation
80
611 Gradient in the Sylvester Subspace
82
612 From Matrices to zTransforms
84
613 SelfClosed and NonSelfClosed Natural Gradients
87
614 From zTransforms Back to the Time Domain
89
615 Application to NGSOSBSS
91
Which Natural Gradient is Best?
93
62 Online Adaptation
97
622 SampleWise Adaptation
98
72 Local Stability
121
73 Summary and Conclusion
123
Comparison of LCMV Beamforming and SecondOrder Statistics BSS
125
81 Properties of the Cost Functions
126
812 On the Estimation Variance
128
82 Complexity
133
821 NLMS Complexity
134
822 Complexity of NGSOSBSS Algorithms
135
823 Comparison of NLMS and NGSOSBSS Complexities
138
83 Links with the ILMS Algorithm
140
84 Experimental Comparison
141
85 Summary and Conclusion
145
Combining SecondOrder Statistics BSS and LCMV Beamforming
147
91 Existing Combinations
148
92 BSS and Geometric Prior Information
149
921 Causality Information
150
922 Prior Information on the Source Direction of Arrival
151
923 Geometric Information at the Initialization
154
924 Geometric Information as a Soft Constraint
156
925 Geometric Information as a Preprocessing
160
93 Combining SOSBSS and the Power Criterion
163
Prior Information and the Power Criterion
165
95 Experimental Results on Automatic Speech Recognition
167
96 Summary and Conclusion
171
Summary and Conclusions
173
Experimental Setups
179
A3 Acoustic Characteristics of the Car Cabin
181
Far and FreeField Acoustic Propagation Model and Null Beamforming
184
B2 Null Beamforming
186
The RGSC According to Hoshuyama et al
189
C2 RGSC for the TwoElement Distributed Array Mounted on the Car Ceiling
191
GSC vs RGSC
192
C31 Experiments with the FourElement Compact Array Mounted in the RearView Mirror
194
C32 Experiments with the TwoElement Distributed Array Mounted on the Car Ceiling
195
C4 Conclusion
196
Stability Analysis
198
D2 Linearization of the NGSOSBSS Updates
200
D3 Local Stability Conditions
203
Notations
207
References
215
Index
222
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