Hänsler / Schmidt Topics in Acoustic Echo and Noise Control

Selected Methods for the Cancellation of Acoustical Echoes, the Reduction of Background Noise, and Speech Processing
1. Auflage 2006
ISBN: 978-3-540-33213-8
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 642 Seiten

This book treats important topics in 'Acoustic Echo and Noise Control' and reports 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, written by an international team of highly qualified experts, concentrates on the modern and advanced methods.

Autoren/Hrsg.

Hänsler, Eberhard

Schmidt, Gerhard

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;List of Contributors;13
4;Abbreviations and Acronyms;15
5;Part I Introduction;20
5.1;1 Acoustic Echo and Noise Control – Where did we come from and where are we going?;21
5.1.1;1.1 The Journey to Maturity;21
5.1.1.1;1.1.1 The Problems to be Solved;21
5.1.1.1.1;1.1.1.1 Voice Controlled Switch;22
5.1.1.1.2;1.1.1.2 Center Clipper;23
5.1.1.1.3;1.1.1.3 Frequency Shift;24
5.1.1.1.4;1.1.1.4 Echo Cancellation and Echo Suppression;24
5.1.1.1.5;1.1.1.5 Echo Cancellation;26
5.1.1.1.6;1.1.1.6 Control of the Filter Adaptation;27
5.1.1.1.7;1.1.1.7 Echo and Noise Suppression;28
5.1.2;1.2 State of the Art;28
5.1.3;1.3 Outline of this Book;30
5.1.4;References;33
6;Part II Multi-Microphone Processing;35
6.1;2 Joint Optimization of Acoustic Echo Cancellation and Adaptive Beamforming;36
6.1.1;2.1 Introduction;36
6.1.2;2.2 Concepts for Joint Acoustic Echo Cancellation and Adaptive Beamforming;38
6.1.2.1;2.2.1 Acoustic Echo Cancellation;38
6.1.2.2;2.2.2 Adaptive Beamforming;40
6.1.2.3;2.2.3 Joint Acoustic Echo Cancellation and Adaptive Beamforming;42
6.1.3;2.3 Joint Optimization of Acoustic Echo Cancellation and Adaptive Beamforming;44
6.1.4;2.4 Implementation;51
6.1.4.1;2.4.1 Robust Generalized Sidelobe Canceller (RGSC);51
6.1.4.1.1;2.4.1.1 Quiescent weight vector;52
6.1.4.1.2;2.4.1.2 Blocking Matrix;52
6.1.4.1.3;2.4.1.3 Interference Canceller;52
6.1.4.1.4;2.4.1.4 Adaptation Control;53
6.1.4.2;2.4.2 Acoustic Echo Canceller;53
6.1.4.3;2.4.3 Computational Complexity;53
6.1.5;2.5 Experimental Results;55
6.1.5.1;2.5.1 Time-Invariant Echo Paths and Time-Invariant Source Position;55
6.1.5.2;2.5.2 Time-Varying Echo Path and Time-Varying Source Position;57
6.1.5.3;2.5.3 Reverberation Time;59
6.1.6;2.6 Conclusion;60
6.1.7;References;61
6.2;3 Blind Source Separation of Convolutive Mixtures of Audio Signals in Frequency Domain;68
6.2.1;3.1 Introduction;68
6.2.2;3.2 Blind Source Separation for Convolutive Mixtures;70
6.2.3;3.3 Overview of Frequency-Domain Approach;72
6.2.4;3.4 Complex-Valued Independent Component Analysis;75
6.2.5;3.5 Separation Mechanism of Blind Source Separation;77
6.2.6;3.6 Source Localization;78
6.2.6.1;3.6.1 Basic Theory of Near.eld Model;79
6.2.6.2;3.6.2 Direction of Arrival Estimation with Far-.eld Model;81
6.2.7;3.7 Permutation Alignment;84
6.2.7.1;3.7.1 Localization Approach;85
6.2.7.2;3.7.2 Correlation Approach;86
6.2.7.3;3.7.3 Integrated Method;87
6.2.8;3.8 Scaling Alignment;89
6.2.9;3.9 Spectral Smoothing;89
6.2.9.1;3.9.1 Windowing;90
6.2.9.2;3.9.2 Minimizing Error by Adjusting Scaling Ambiguity;91
6.2.10;3.10 Experimental Results;92
6.2.10.1;3.10.1 2 × 2, 3 × 3, ×;93
6.2.10.2;3.10.2 6 × 8 with Planar Array;97
6.2.10.3;3.10.3 2 × 2 Moving Sources;100
6.2.11;3.11 Conclusion;102
6.2.12;References;103
6.3;4 Localization and Tracking of Acoustical Sources;107
6.3.1;4.1 Introduction;107
6.3.2;4.2 Source Localization Using the Generalized Cross-Correlation Function;110
6.3.3;4.3 Source Localization Based on Interaural Time Differences;113
6.3.4;4.4 Source Localization Using Adaptive Filters;119
6.3.5;4.5 Some Remarks on Algorithm Selection;126
6.3.6;4.6 Frequency-Domain Adaptive Beamformer with Speaker Tracking;127
6.3.7;4.7 Conclusions;136
6.3.8;Acknowledgements;136
6.3.9;References;137
7;Part III Echo Cancellation;139
7.1;5 Adaptive Algorithms for the Identi.cation of Sparse Impulse Responses;140
7.1.1;5.1 Introduction;140
7.1.2;5.2 Notation and De.nitions;141
7.1.3;5.3 Sparseness Measure;143
7.1.4;5.4 The NLMS, PNLMS, and IPNLMS Algorithms;145
7.1.5;5.5 Universal Criterion;147
7.1.5.1;5.5.1 Linear Update;148
7.1.5.2;5.5.2 nonlinear Update;149
7.1.6;5.6 Exponentiated Gradient Algorithms;150
7.1.6.1;5.6.1 The EG Algorithm for Positive Weights;150
7.1.6.2;5.6.2 The EG± Algorithm for Positive and Negative Weights;151
7.1.6.3;5.6.3 The Exponentiated RLS (ERLS) Algorithm;154
7.1.7;5.7 The Lambert W Function Based Gradient Algorithm;155
7.1.8;5.8 Some Important Links Among Algorithms;156
7.1.8.1;5.8.1 Link Between NLMS and EG± Algorithms;156
7.1.8.2;5.8.2 Link Between IPNLMS and EG± Algorithms;157
7.1.8.3;5.8.3 Link Between LWG and EG± Algorithms;159
7.1.9;5.9 Simulations;160
7.1.10;5.10 Conclusions;164
7.1.11;References;166
7.2;6 Selective-Tap Adaptive Algorithms for Echo Cancellation;169
7.2.1;6.1 Introduction;170
7.2.2;6.2 Sequential and Periodic Tap Selection;171
7.2.3;6.3 MMax Tap Selection;173
7.2.3.1;6.3.1 The MMax-NLMS Algorithm;174
7.2.3.2;6.3.2 Dependence of Convergence Rate on MMax Tap Selection;175
7.2.3.3;6.3.3 The MMax A.ne Projection Algorithm;176
7.2.3.4;6.3.4 The MMax Recursive Least Squares Algorithm;177
7.2.3.5;6.3.5 Computational Complexity;179
7.2.4;6.4 Selective Partial Update Tap Selection;180
7.2.5;6.5 Performance Comparison for Single-Channel;182
7.2.6;Selective-Tap algorithms;182
7.2.7;6.6 Convergence Analysis;182
7.2.7.1;6.6.1 Non-stationary System Model;183
7.2.7.2;6.6.2 Mean Square Misalignment for NLMS with;185
7.2.7.3;6.6.3 Mean Square Misalignment for MMax-NLMS with;187
7.2.7.4;6.6.4 Simulation Results for single channel NLMS and;189
7.2.8;6.7 Sparse Partial Update NLMS;193
7.2.9;6.8 multichannel Selective-Tap Algorithms for Stereophonic Acoustic Echo Cancellation;195
7.2.9.1;6.8.1 Overview and Rationale;195
7.2.9.2;6.8.2 Reducing Interchannel Coherence using Tap Selection;196
7.2.10;6.9 Exclusive Maximum Tap Selection;199
7.2.10.1;6.9.1 Formulation and Realization using Exhaustive Search;199
7.2.10.2;6.9.2 E.cient Realization;202
7.2.11;6.10 Exclusive Maximum Adaptive Filters;204
7.2.11.1;6.10.1 XM-NLMS Algorithm;205
7.2.11.2;6.10.2 XMNL-NLMS Algorithm;205
7.2.11.3;6.10.3 XMNL-AP Algorithm;206
7.2.11.4;6.10.4 XMNL-RLS Algorithm;206
7.2.12;6.11 SAEC Simulation Results;206
7.2.12.1;6.11.1 Experimental Setup;206
7.2.12.2;6.11.2 NLMS Simulations;206
7.2.12.3;6.11.3 AP Simulations;207
7.2.12.4;6.11.4 RLS Simulations;208
7.2.13;6.12 Discussion and Conclusion;208
7.2.14;A Appendices;211
7.2.14.1;A.1 Algorithm Summary Tables;211
7.2.14.2;A.2 Fourth-order Factorization for Zero Mean Gaussian Variables;214
7.2.15;References;214
7.2.15.1;1, 266–270, New Orleans, LA, UAS, 1990.;215
7.2.15.2;1, 448–451, Istanbul, Turkey, 2000.;215
7.2.15.3;5, 373–376, Hong Kong, 2003.;217
7.2.15.4;1, 950–954, 2004.;217
7.2.15.5;6, 3685–3688, Seattle, Washington, USA, 1998.;217
7.2.15.6;52(4), 938–949, 2004.;217
7.3;7 Nonlinear Acoustic Echo Cancellation;218
7.3.1;7.1 Introduction;218
7.3.2;7.2 Nonlinear Acoustic Echo Paths;220
7.3.3;7.3 Volterra Filters;224
7.3.3.1;7.3.1 Application to Cascaded Structures;228
7.3.3.2;7.3.2 Time-domain Adaptive Volterra Filters;230
7.3.3.3;7.3.3 Multidelay Adaptive Volterra Filters;239
7.3.3.4;7.3.4 Application to Real Systems;246
7.3.4;7.4 Power Filters;250
7.3.4.1;7.4.1 Application to Cascaded Structures;252
7.3.4.2;7.4.2 Adaptive Orthogonalized Power Filters;256
7.3.4.3;7.4.3 Application to Real Systems;268
7.3.5;7.5 Conclusions;270
7.3.6;References;272
7.4;8 Intelligent Control Strategies for Hands-Free Telephones;275
7.4.1;8.1 Introduction;275
7.4.1.1;8.1.1 State Representation of a Hands-Free Telephone;275
7.4.1.2;8.1.2 Combination of Control Algorithms;278
7.4.2;8.2 Fuzzy Systems;279
7.4.2.1;8.2.1 Classic Versus Fuzzy Detector – Example: The Correlation Coefficient;280
7.4.2.2;8.2.2 Application of Fuzzy Systems in a step-size Control for an Echo Canceller;283
7.4.3;8.3 Learning Vector Quantization;288
7.4.3.1;8.3.1 Example: Fuzzy LVQ for State Detection in a Hands-free Telephone;290
7.4.4;8.4 Prerequisites for Automatic Optimization of Control Algorithms: Optimum Step Size and Cost Function;294
7.4.4.1;8.4.1 Optimum Step size for Network Training;294
7.4.4.2;8.4.2 Cost Function;297
7.4.5;8.5 Radial Basis Function Network for Step-Size Control Network for State Detection;317
7.4.6;8.6 Radial Basis Function;317
7.4.6.1;8.6.1 State Classi.cation;318
7.4.6.1.1;8.6.1.1 Estimation of Probability of States;318
7.4.6.1.2;8.6.1.2 Probability of State Characteristics;323
7.4.6.2;8.6.2 Reliability of Detectors;327
7.4.6.3;8.6.3 Conclusions;329
7.4.7;References;330
8;Part IV Noise Reduction;334
8.1;9 Noise Reduction;335
8.1.1;9.1 Introduction;335
8.1.1.1;9.1.1 Noise and Speech;335
8.1.1.2;9.1.2 Types of Disturbances, Aim of Reduction;336
8.1.1.3;9.1.3 Noise-Reduction Approaches;336
8.1.1.4;9.1.4 Wiener Filter and Spectral Subtraction;337
8.1.2;9.2 Optimum-Filter Design in the Time Domain;339
8.1.2.1;9.2.1 Mean-Square Error Minimization;339
8.1.2.2;9.2.2 Approximate FIR-Filter Solution;340
8.1.3;9.3 Wiener-Filter Description in the Frequency Domain;342
8.1.3.1;9.3.1 Optimum Frequency Response;342
8.1.3.2;9.3.2 Approximate FIR-Filter Solution;342
8.1.4;9.4 Examples and Filtering E.ects;343
8.1.4.1;9.4.1 “Low-Pass Signal” plus “Band-Stop Noise”;343
8.1.4.2;9.4.2 Decaying Spectrum plus Wide-Band Noise;346
8.1.5;9.5 Wiener-Filter Realizations;346
8.1.6;9.6 Spectral Subtraction: Principles and Realization;349
8.1.6.1;9.6.1 De.nition and Variants;349
8.1.6.2;9.6.2 Relation with Wiener Filtering;350
8.1.6.3;9.6.3 Realization;352
8.1.7;9.7 Noise Power Density Spectrum Estimation;353
8.1.7.1;9.7.1 Noise Measurement in Speech Pauses;354
8.1.7.2;9.7.2 Continuous Noise Measurements;355
8.1.7.3;9.7.3 Minimum Statistics;355
8.1.7.4;9.7.4 Improved Instationarity Tracking;357
8.1.8;9.8 Subtraction and Weighting Rules;358
8.1.8.1;9.8.1 Magnitude and Power Subtraction;358
8.1.8.2;9.8.2 Musical Noise;359
8.1.8.3;9.8.3 Noise Floor, Over-Estimation, and Non-Linear Subtraction;359
8.1.8.4;9.8.4 Approaches Based on Statistical Models of Signal and Noise;360
8.1.9;9.9 Spectral Analysis and Synthesis;361
8.1.9.1;9.9.1 DFT and IDFT;361
8.1.9.2;9.9.2 Generalizations;361
8.1.9.3;9.9.3 Complex-Modulated Filterbank;362
8.1.9.4;9.9.4 Real-Valued Filterbanks;365
8.1.9.5;9.9.5 Non-Equispaced Frequency Bands;366
8.1.9.5.1;9.9.5.1 Partial Recombination;367
8.1.9.5.2;9.9.5.2 Warped PPN-FFT;367
8.1.9.5.3;9.9.5.3 Pruned Tree Structure;370
8.1.9.5.4;9.9.5.4 Wavelet-Related Analysis-Synthesis Systems;370
8.1.9.6;9.9.6 Adaptive Bandwidths;374
8.1.9.6.1;9.9.6.1 Motivation;374
8.1.9.6.2;9.9.6.2 Possibilities;374
8.1.9.6.3;9.9.6.3 Efficient Realization;376
8.1.10;9.10 System Configurations, Experiments, and Comparisons;377
8.1.10.1;9.10.1 Status;377
8.1.10.2;9.10.2 Examples;378
8.1.10.2.1;9.10.2.1 Uniform vs. Non-uniform Bandwidths;378
8.1.10.2.2;9.10.2.2 Fixed vs. Adaptive Bandwidths;382
8.1.10.2.3;9.10.2.3 Noise Instationarity;385
8.1.11;9.11 Further Problems and Ideas, Concluding Remarks;386
8.1.12;References;389
8.1.12.1;T-SA-9(5), 504–512, 2001.;393
8.1.12.2;7, 126–137, 1999.;394
8.2;10 Noise Reduction with Kalman-Filters for Hands-Free Car Phones Based on Parametric Spectral Speech and Noise Estimates;395
8.2.1;10.1 Introduction;395
8.2.2;10.2 Speech and Car Noise Analysis;397
8.2.2.1;10.2.1 Car Noise Analysis;397
8.2.2.1.1;10.2.1.1 Engine Noise;399
8.2.2.1.2;10.2.1.2 Wind Noise;399
8.2.2.1.3;10.2.1.3 Tyre Noise;400
8.2.2.2;10.2.2 Speech Analysis;401
8.2.3;10.3 Theoretical Basics;403
8.2.3.1;10.3.1 Kalman Filters for Colored Noise Signals;403
8.2.3.2;10.3.2 Parametric Spectral Estimation;409
8.2.4;10.4 Application of Kalman Filters for Noise Reduction;414
8.2.4.1;10.4.1 Subband Processing;415
8.2.4.2;10.4.2 AR Model Estimation for Speech and Noise;416
8.2.4.3;10.4.3 Pitch-Adaptive Enhanced Speech Model Estimation;427
8.2.5;10.5 Comparison of the Results with Classical Frequency Domain Noise Reduction Approaches;430
8.2.6;10.6 Conclusions;435
8.2.7;References;436
9;Part V Selected Applications;438
9.1;11 Evaluation of Algorithms for Speech Enhancement;439
9.1.1;11.1 The Focus of this Chapter;439
9.1.2;11.2 Objective Tests for Noise Suppression;440
9.1.2.1;11.2.1 Measuring the Quality of Noise Suppression Systems;442
9.1.2.2;11.2.2 Distance Measures;445
9.1.2.2.1;11.2.2.1 Cepstral Distance;445
9.1.2.2.2;11.2.2.2 Itakura Measure;448
9.1.2.2.3;11.2.2.3 Itakura-Saito Measure;450
9.1.2.3;11.2.3 Noise Characteristics;451
9.1.2.3.1;11.2.3.1 Noise Attenuation;451
9.1.2.3.2;11.2.3.2 Musical Noise;451
9.1.2.3.3;11.2.3.3 Di.erence in Power Level;452
9.1.2.4;11.2.4 Psycho-Acoustic Methods;453
9.1.2.5;11.2.5 Coherence Between Instrumental Measures and Listening Tests;454
9.1.2.5.1;11.2.5.1 Rank Correlation;454
9.1.2.5.2;11.2.5.2 Judging Quotient;455
9.1.2.5.3;11.2.5.3 Results;456
9.1.3;11.3 Comparison Mean Opinion Scores (CMOS);457
9.1.3.1;11.3.1 Example;460
9.1.3.1.1;11.3.1.1 Basics of Bandwidth Extension Algorithms;460
9.1.3.1.2;11.3.1.2 Performing the CMOS Test;463
9.1.3.1.3;11.3.1.3 Evaluation of the Test;465
9.1.3.1.4;11.3.1.4 Remark;468
9.1.3.2;11.3.2 Statistical Analysis;469
9.1.3.2.1;11.3.2.1 Analysis of the Two-Level Test;469
9.1.3.2.2;11.3.2.2 Analysis of the Seven-Level Test;474
9.1.4;11.4 Rhyme Tests;476
9.1.4.1;11.4.1 Performing a Rhyme Test;478
9.1.4.2;11.4.2 Example;481
9.1.4.2.1;11.4.2.1 Basics of In-Car Communication Systems;481
9.1.4.2.2;11.4.2.2 Rhyme Test for In-Car Communication Systems;483
9.1.4.3;11.4.3 Statistical Analysis;487
9.1.4.3.1;11.4.3.1 Hypotheses;487
9.1.4.3.2;11.4.3.2 Results;488
9.1.5;11.5 Outlook;489
9.1.6;References;490
9.1.6.1;2(4), 598–614, 1994.;490
9.1.6.2;57(4/5), 257–267, 1989 (in German).;492
9.2;12 An Auditory Scene Analysis Approach to Monaural Speech Segregation;493
9.2.1;12.1 Introduction;493
9.2.2;12.2 Computational Auditory Scene Analysis;496
9.2.2.1;12.2.1 Computational Goal of CASA;496
9.2.2.2;12.2.2 Motivation and Overview of the Approach;497
9.2.3;12.3 Peripheral Analysis and Feature Extraction;498
9.2.3.1;12.3.1 Auditory Periphery;499
9.2.3.2;12.3.2 Correlogram and Cross-Channel Correlation;499
9.2.3.3;12.3.3 Onset and O.set;500
9.2.3.4;12.3.4 Pitch Determination;502
9.2.4;12.4 Auditory Segmentation;505
9.2.4.1;12.4.1 Segmentation for Voiced Speech;505
9.2.4.2;12.4.2 Segmentation Based on Onset/O.set Analysis;506
9.2.5;12.5 Voiced Speech Grouping;507
9.2.6;12.6 Unvoiced Speech Grouping;511
9.2.6.1;12.6.1 Segregation of Stop Consonants;512
9.2.6.2;12.6.2 Grouping of Fricatives and A.ricates;513
9.2.7;12.7 Concluding Remarks;516
9.2.8;Appendix: Voiced Speech Segregation Algorithm;517
9.2.9;Acknowledgments;520
9.2.10;References;520
9.2.10.1;2, 749–752, 2003.;522
9.2.10.2;1, 357–360, Albuquerque, NM, USA, 1990.;523
9.3;13 Wave Field Synthesis Techniques for Spatial Sound Reproduction;524
9.3.1;13.1 Introduction;524
9.3.2;13.2 Elements from the Foundations of Acoustics;525
9.3.2.1;13.2.1 Coordinate Systems;525
9.3.2.1.1;13.2.1.1 Two-Dimensional Coordinates;525
9.3.2.1.2;13.2.1.2 Three-Dimensional Coordinates;526
9.3.2.2;13.2.2 The Wave Equation;526
9.3.2.2.1;13.2.2.1 Plane Wave Solution;526
9.3.2.2.2;13.2.2.2 Green’s Functions;527
9.3.2.2.3;13.2.2.3 Relation Between the Green’s Functions for Line and Point Sources for the Free Field Case;530
9.3.2.3;13.2.3 Kirchhoff-Helmholtz Integral;532
9.3.2.3.1;13.2.3.1 Kirchho.-Helmholtz Integral for a General 3D Volume;532
9.3.2.3.2;13.2.3.2 Kirchho.-Helmholtz Integral for a Prism;532
9.3.3;13.3 Wave Field Synthesis;534
9.3.3.1;13.3.1 Introduction;534
9.3.3.2;13.3.2 Kirchho.-Helmholtz Integral based Sound Reproduction;535
9.3.3.3;13.3.3 Monopole and Dipole Sources;537
9.3.3.4;13.3.4 Reduction to Two Spatial Dimensions;539
9.3.3.5;13.3.5 Spatial Sampling;540
9.3.3.6;13.3.6 Driving Signals;541
9.3.3.6.1;13.3.6.1 Boundary Conditions;541
9.3.3.6.2;13.3.6.2 Determination of the Normal Derivative;543
9.3.3.6.3;13.3.6.3 Independence of the Driving Signals from the Listener Position;546
9.3.3.7;13.3.7 Signal Processing Structure;546
9.3.4;13.4 Implementation of a Wave Field Synthesis System;547
9.3.5;13.5 Conclusions;549
9.3.6;References;550
9.4;14 Signal Processing for In-Car Communication Systems;553
9.4.1;14.1 Basics;555
9.4.1.1;14.1.1 Communication without Intercom Systems;555
9.4.1.2;14.1.2 Communication with Intercom Systems;560
9.4.1.2.1;14.1.2.1 Frequency Response of the Closed-Loop System;561
9.4.1.2.2;14.1.2.2 Transmission from Speaking to Listening Passenger;563
9.4.1.2.3;14.1.2.3 Transmission from Speaking to Speaking Passenger;564
9.4.2;14.2 Signal Processing for Intercom Systems;566
9.4.2.1;14.2.1 Processing Structures;568
9.4.2.2;14.2.2 Preprocessing;568
9.4.2.3;14.2.3 Beamforming;569
9.4.2.4;14.2.4 Echo Cancellation;571
9.4.2.4.1;14.2.4.1 Cancellation of the Output Signal of the Car Radio;572
9.4.2.4.2;14.2.4.2 Cancellation of the Output Signal of the Intercom System;575
9.4.2.5;14.2.5 Feedback Cancellation;576
9.4.2.6;14.2.6 Feedback Suppression;577
9.4.2.7;14.2.7 Combining Feedback Cancellation and Feedback Suppression;579
9.4.2.7.1;14.2.7.1 Extensions;582
9.4.2.8;14.2.8 Gain Control;584
9.4.2.8.1;14.2.8.1 Basic Control Structure;584
9.4.2.8.2;14.2.8.2 Automatic Gain Control;586
9.4.2.8.3;14.2.8.3 Speech Activity Controlled Attenuation;587
9.4.2.8.4;14.2.8.4 Adjustment of the Playback Volume;587
9.4.2.9;14.2.9 Loudspeaker Equalization;588
9.4.2.10;14.2.10 Further Signal Processing Units;590
9.4.3;14.3 Evaluation of Intercom Systems;591
9.4.3.1;14.3.1 Subjective Methods;592
9.4.3.2;14.3.2 Rhyme Tests;592
9.4.3.3;14.3.3 Comparison Mean Opinion Scores;594
9.4.3.4;14.3.4 Objective Methods;595
9.4.3.5;14.3.4.1 Improvements for the Listening Passengers;595
9.4.3.6;14.3.4.2 Distortions for the Speaking Passenger;598
9.4.4;14.4 A Real System;598
9.4.5;14.5 Conclusions and Outlook;602
9.4.6;References;602
9.5;15 Applications of Adaptive Signal Processing Methods in High-End Hearing Aids;605
9.5.1;15.1 Introduction;605
9.5.2;15.2 Directional Microphones;606
9.5.2.1;15.2.1 First-Order Di.erential Arrays;607
9.5.2.2;15.2.2 Second-Order Di.erential Arrays;610
9.5.3;15.3 Noise Reduction;614
9.5.3.1;15.3.1 Long-Term Smoothed, Modulation Frequency Based Noise Reduction;615
9.5.3.1.1;15.3.1.1 Theoretical Basis;615
9.5.3.1.2;15.3.1.2 Computational E.cient Realization;616
9.5.3.2;15.3.2 Wiener-Filter Based, Short-Term Smoothed Noise Reduction Methods;618
9.5.3.3;15.3.3 Ephraim-Malah Based, Short-Term Smoothed Noise Reduction Methods;621
9.5.3.4;15.3.4 Future Trends;622
9.5.4;15.4 Multi-Band Compression;623
9.5.4.1;15.4.1 State-of-the-Art;623
9.5.4.2;15.4.2 Future Trends;625
9.5.5;15.5 Feedback Cancellation;626
9.5.5.1;15.5.1 Feedback Suppression: Dynamic and Selective Attenuation of Feedback Components;629
9.5.5.2;15.5.2 Feedback Compensation;629
9.5.6;15.6 Classification;633
9.5.6.1;15.6.1 Basic Structure of Monaural Classi.cation;633
9.5.6.2;15.6.2 Binaural Classi.cation;637
9.5.6.3;15.6.3 Future Trends;637
9.5.7;15.7 Summary;638
9.5.8;References;638
10;Index;643