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Summary
Summary
In this book, an international team of highly qualified experts treats important topics in "Acoustic Echo and Noise Control" and report the latest developments. Methods for enhancing the quality of transmitted speech signals are gaining growing attention in universities and in industrial development laboratories.
This book is organized in five parts: Part I gives a short introduction to acoustic echo and noise control. Part II deals with multi-microphone processing. In Part III, advanced methods for both linear and nonlinear echo cancellation are presented, and techniques for intelligent control of hands-free telephones are introduced. Part IV is devoted to noise reduction procedures. An in-depth treatment of conventional and of advanced methods is given, followed by a model based approach using Kalman filters. Finally, in Part V, selected applications of acoustic echo and noise control as well as speech and audio processing in general are outlined. Topics such as auditory scene analysis, wave field synthesis for spatial sound reproduction, in-car communication systems, and hearing aids are treated.
Table of Contents
Abbreviations and Acronyms | p. XV |
Part I Introduction | |
1 Acoustic Echo and Noise Control - Where did we come from and where are we going?E. Hänsler and G. Schmidt | |
1.1 The Journey to Maturity | p. 3 |
1.2 State of the Art | p. 10 |
1.3 Outline of this Book | p. 12 |
Part II Multi-Microphone Processing | |
2 Joint Optimization of Acoustic Echo Cancellation and Adaptive BeamformingW. Herbordt and W. Kellermann and S. Nakamura | |
2.1 Introduction | p. 19 |
2.2 Concepts for Joint Acoustic Echo Cancellation and Adaptive Beamforming | p. 21 |
2.3 Joint Optimization of Acoustic Echo Cancellation and Adaptive Beamforming | p. 27 |
2.4 Implementation | p. 34 |
2.5 Experimental Results | p. 38 |
2.6 Conclusion | p. 43 |
3 Blind Source Separation of Convolutive Mixtures of Audio Signals in Frequency DomainS. Makino and H. Sawada and R. Mukai and S. Araki | |
3.1 Introduction | p. 51 |
3.2 Blind Source Separation for Convolutive Mixtures | p. 53 |
3.3 Overview of Frequency-Domain Approach | p. 55 |
3.4 Complex-Valued Independent Component Analysis | p. 58 |
3.5 Separation Mechanism of Blind Source Separation | p. 60 |
3.6 Source Localization | p. 61 |
3.7 Permutation Alignment | p. 67 |
3.8 Scaling Alignment | p. 72 |
3.9 Spectral Smoothing | p. 72 |
3.10 Experimental Results | p. 75 |
3.11 Conclusion | p. 85 |
4 Localization and Tracking of Acoustical SourcesG. Doblinger | |
4.1 Introduction | p. 91 |
4.2 Source Localization Using the Generalized Cross-Correlation Function | p. 94 |
4.3 Source Localization Based on Interaural Time Differences | p. 97 |
4.4 Source Localization Using Adaptive Filters | p. 103 |
4.5 Some Remarks on Algorithm Selection | p. 110 |
4.6 Frequency-Domain Adaptive Beamformer with Speaker Tracking | p. 111 |
4.7 Conclusions | p. 120 |
Part III Echo Cancellation | |
5 Adaptive Algorithms for the Identification of Sparse Impulse ResponsesJ. Benesty and Y. Huang and J. Chen and P. A.Naylor | |
5.1 Introduction | p. 125 |
5.2 Notation and Definitions | p. 126 |
5.3 Sparseness Measure | p. 128 |
5.4 The NLMS, PNLMS, and IPNLMS Algorithms | p. 130 |
5.5 Universal Criterion | p. 132 |
5.6 Exponentiated Gradient Algorithms | p. 135 |
5.7 The Lambert W Function Based Gradient Algorithm | p. 140 |
5.8 Some Important Links Among Algorithms | p. 141 |
5.9 Simulations | p. 145 |
5.10 Conclusions | p. 149 |
6 Selective-Tap Adaptive Algorithms for Echo CancellationP.A. Naylor and A. W.H. Khong | |
6.1 Introduction | p. 156 |
6.2 Sequential and Periodic Tap Selection | p. 157 |
6.3 M Max Tap Selection | p. 159 |
6.4 Selective Partial Update Tap Selection | p. 166 |
6.5 Performance Comparison for Single-Channel Selective-Tap algorithms | p. 168 |
6.6 Convergence Analysis | p. 168 |
6.7 Sparse Partial Update NLMS | p. 179 |
6.8 multichannel Selective-Tap Algorithms for Stereophonic Acoustic Echo Cancellation | p. 181 |
6.9 Exclusive Maximum Tap Selection | p. 185 |
6.10 Exclusive Maximum Adaptive Filters | p. 190 |
6.11 SAEC Simulation Results | p. 192 |
6.12 Discussion and Conclusion | p. 194 |
A Appendices | p. 197 |
7 Nonlinear Acoustic Echo CancellationF. Küch and W. Kellermann | |
7.1 Introduction | p. 205 |
7.2 Nonlinear Acoustic Echo Paths | p. 207 |
7.3 Volterra Filters | p. 211 |
7.4 Power Filters | p. 237 |
7.5 Conclusions | p. 257 |
8 Intelligent Control Strategies for Hands-Free TelephonesC. Breining and A. Mader | |
8.1 Introduction | p. 263 |
8.2 Fuzzy Systems | p. 267 |
8.3 Learning Vector Quantization | p. 276 |
8.4 Prerequisites for Automatic Optimization of Control Algorithms: Optimum Step Size and Cost Function | p. 282 |
8.5 Radial Basis Function Network for Step-Size Control | p. 288 |
8.6 Radial Basis Function Network for State Detection | p. 305 |
Part IV Noise Reduction | |
9 Noise ReductionU. Heute | |
9.1 Introduction | p. 325 |
9.2 Optimum-Filter Design in the Time Domain | p. 329 |
9.3 Wiener-Filter Description in the Frequency Domain | p. 332 |
9.4 Examples and Filtering Effects | p. 333 |
9.5 Wiener-Filter Realizations | p. 336 |
9.6 Spectral Subtraction: Principles and Realization | p. 339 |
9.7 Noise Power Density Spectrum Estimation | p. 343 |
9.8 Subtraction and Weighting Rules | p. 348 |
9.9 Spectral Analysis and Synthesis | p. 351 |
9.10 System Configurations, Experiments, and Comparisons | p. 367 |
9.11 Further Problems and Ideas, Concluding Remarks | p. 376 |
10 Noise Reduction with Kalman-Filters for Hands-Free Car Phones Based on Parametric Spectral Speech and Noise EstimatesH. Puder | |
10.1 Introduction | p. 385 |
10.2 Speech and Car Noise Analysis | p. 387 |
10.3 Theoretical Basics | p. 393 |
10.4 Application of Kalman Filters for Noise Reduction | p. 404 |
10.5 Comparison of the Results with Classical Frequency Domain Noise Reduction Approaches | p. 420 |
10.6 Conclusions | p. 425 |
Part V Selected Applications | |
11 Evaluation of Algorithms for Speech EnhancementP. Dreiseitel and G. Schmidt | |
11.1 The Focus of this Chapter | p. 431 |
11.2 Objective Tests for Noise Suppression | p. 432 |
11.3 Comparison Mean Opinion Scores (CMOS) | p. 449 |
11.4 Rhyme Tests | p. 468 |
11.5 Outlook | p. 481 |
12 An Auditory Scene Analysis Approach to Monaural Speech SegregationG. Hu and D. L.Wang | |
12.1 Introduction | p. 485 |
12.2 Computational Auditory Scene Analysis | p. 488 |
12.3 Peripheral Analysis and Feature Extraction | p. 490 |
12.4 Auditory Segmentation | p. 497 |
12.5 Voiced Speech Grouping | p. 499 |
12.6 Unvoiced Speech Grouping | p. 503 |
12.7 Concluding Remarks | p. 508 |
13 Wave Field Synthesis Techniques for Spatial Sound ReproductionR. Rabenstein and S. Spors and P. Steffen | |
13.1 Introduction | p. 517 |
13.2 Elements from the Foundations of Acoustics | p. 518 |
13.3 Wave Field Synthesis | p. 527 |
13.4 Implementation of a Wave Field Synthesis System | p. 540 |
13.5 Conclusions | p. 542 |
14 Signal Processing for In-Car Communication SystemsG. Schmidt and T. Haulick | |
14.1 Basics | p. 549 |
14.2 Signal Processing for Intercom Systems | p. 560 |
14.3 Evaluation of Intercom Systems | p. 585 |
14.4 A Real System | p. 592 |
14.5 Conclusions and Outlook | p. 596 |
15 Applications of Adaptive Signal Processing Methods in High-End Hearing AidsV. Hamacher and E. Fischer and U. Kornagel and H. Puder | |
15.1 Introduction | p. 599 |
15.2 Directional Microphones | p. 600 |
15.3 Noise Reduction | p. 608 |
15.4 Multi-Band Compression | p. 617 |
15.5 Feedback Cancellation | p. 620 |
15.6 Classification | p. 627 |
15.7 Summary | p. 632 |
Index | p. 637 |