Research Catalog

Title

Acoustic MIMO signal processing / Yiteng (Arden) Huang, Jacob Benesty, Jingdong Chen.

Author

Huang, Yiteng, 1972-

Publication

New York : Springer, 2006.

Supplementary Content

Publisher description

Items in the Library & Off-site

Holdings

Details

Additional Authors

• Benesty, Jacob.
• Chen, J. (Jingdong)

Description

xiv, 378 pages : illustrations; 25 cm.

Series Statement

Signals and communication technology

Uniform Title

Signals and communication technology.

Alternative Title

Acoustic multiple-input multiple-output signal processing

Subjects

• MIMO systems
• Signal processing > Digital techniques > Mathematics
• Speech processing systems

Bibliography (note)

• Includes bibliographical references and index.

Contents

• 1. Introduction -- 1.1. Acoustic MIMO Signal Processing -- 1.2. Organization of the Book -- Part I. Theory -- 2. Acoustic MIMO Systems -- 2.1. Signal Models -- 2.1.1. SISO Model -- 2.1.2. SIMO Model -- 2.1.3. MISO Model -- 2.1.4. MIMO Model -- 2.2. Characteristics of Acoustic Channels -- 2.2.1. Linearity and Shift-Invariance -- 2.2.2. FIR Representation -- 2.2.3. Time-Varying Channel Impulse Responses -- 2.2.4. Frequency Selectivity -- 2.2.5. Reverberation Time -- 2.2.6. Channel Invertibility and Minimum-Phase Filter -- 2.2.7. Multichannel Diversity and the Common-Zero Problem -- 2.2.8. Sparse Impulse Response -- 2.3. Measurement and Simulation of MIMO Acoustic Systems -- 2.3.1. Direct Measurement of Acoustic Impulse Responses -- 2.3.2. Image Model for Acoustic Impulse Response Simulation -- 2.4. Summary -- 3. Wiener Filter and Basic Adaptive Algorithms -- 3.1. Introduction -- 3.2. Wiener Filter -- 3.3. Impulse Response Tail Effect -- 3.4. Condition Number -- 3.4.1. Decomposition of the Correlation Matrix -- 3.4.2. Condition Number with the Frobenius Norm -- 3.4.3. Fast Computation of the Condition Number -- 3.5. Basic Adaptive Algorithms -- 3.5.1. Deterministic Algorithm -- 3.5.2. Stochastic Algorithm -- 3.5.3. Sign Algorithms -- 3.6. MIMO Wiener Filter -- 3.7. Numerical Examples -- 3.8. Summary -- 4. Sparse Adaptive Filters -- 4.1. Introduction -- 4.2. Notation and Definitions -- 4.3. The NLMS, PNLMS, and IPNLMS Algorithms -- 4.4. Universal Criterion -- 4.4.1. Linear Update -- 4.4.2. Non-Linear Update -- 4.5. Exponentiated Gradient Algorithms -- 4.5.1. The EG Algorithm for Positive Weights -- 4.5.2. The EG[plus or minus] Algorithm for Positive and Negative Weights -- 4.5.3. The Exponentiated RLS (ERLS) Algorithm -- 4.6. The Lambert W Function Based Gradient Algorithm -- 4.7. Some Important Links Among Algorithms -- 4.7.1. Link Between NLMS and EG[plus or minus] Algorithms -- 4.7.2. Link Between IPNLMS and EG[plus or minus] Algorithms -- 4.7.3. Link Between LWG and EG[plus or minus] Algorithms -- 4.8. Numerical Examples -- 4.9. Summary -- 5. Frequency-Domain Adaptive Filters -- 5.1. Introduction -- 5.2. Derivation of SISO FD Adaptive Algorithms -- 5.2.1. Criterion -- 5.2.2. Normal Equations -- 5.2.3. Adaptive Algorithms -- 5.2.4. Convergence Analysis -- 5.3. Approximation and Special Cases -- 5.3.1. Approximation -- 5.3.2. Special Cases -- 5.4. FD Affine Projection Algorithm -- 5.5. Generalization to the MISO System Case -- 5.6. Numerical Examples -- 5.7. Summary -- 6. Blind Identification of Acoustic MIMO Systems -- 6.1. Introduction -- 6.2. Blind SIMO Identification -- 6.2.1. Identifiability and Principle -- 6.2.2. Constrained Time-Domain Multichannel LMS and Newton Algorithms -- 6.2.3. Unconstrained Multichannel LMS Algorithm with Optimal Step-Size Control -- 6.2.4. Frequency-Domain Unnormalized and Normalized Multichannel LMS Algorithms -- 6.2.5. Adaptive Multichannel Exponentiated Gradient Algorithm -- 6.2.6. Numerical Examples -- 6.3. Blind MIMO Identification -- 6.3.1. Problem Formulation and Background Review -- 6.3.2. Memoryless MIMO System with White Inputs -- 6.3.3. Memoryless MIMO System with Colored Inputs -- 6.3.4. Convolutive MIMO Systems with White Inputs -- 6.3.5. Convolutive MIMO Systems with Colored Inputs -- 6.3.6. Frequency-Domain Blind Identification of Convolutive MIMO Systems and Permutation Inconsistency -- 6.3.7. Convolutive MIMO Systems with White but Quasistationary Inputs -- 6.4. Summary -- 6.5. Appendix. Blind SIMO Identification: A Derivation Directly From the Covariance Matrices of the System Outputs -- 7. Separation and Suppression of Co-Channel and Temporal Interference -- 7.1. Introduction -- 7.2. Separating Co-Channel and Temporal Interference -- 7.2.1. Example: Converison of a 2 x 3 MIMO Systme to Two SIMO Systems -- 7.2.2. Generalization to M x N MIMO Systems with M > 2 and M < N -- 7.3. Suppressing Temporal Interference -- 7.3.1. Direct Inverse (Zero-Forcing) Equalizer -- 7.3.2. MMSE Equalizer -- 7.3.3. MINT Equalizers -- 7.4. Summary -- Part II. Applications -- 8. Acoustic Echo Cancellation and Audio Bridging -- 8.1. Introduction -- 8.2. Network Echo Problem -- 8.3. Single-Channel Acoustic Echo Cancellation -- 8.4. Multichannel Acoustic Echo Cancellation -- 8.4.1. Multi versus Mono -- 8.4.2. Multichannel Identification and the Nonuniqueness Problem -- 8.4.3. Impulse Response Tail Effect -- 8.4.4. Some Different Solutions for Decorrelation -- 8.5. Hybrid Mono/Stereo Acoustic Echo Canceler -- 8.6. Double-Talk Detection -- 8.6.1. Basics -- 8.6.2. Double-Talk Detection Algorithms -- 8.6.3. Performance Evaluation of DTDs -- 8.7. Audio Bridging -- 8.7.1. Principle -- 8.7.2. Interchannel Differences for Synthesizing Stereo Sound -- 8.7.3. Choice of Interchannel Differences for Stereo AEC -- 8.8. Summary -- 9. Time Delay Estimation and Acoustic Source Localization -- 9.1. Time Delay Estimation -- 9.2. Cross-Correlation Method -- 9.3. Magnitude-Difference Method -- 9.4. Maximum Likelihood Method -- 9.5. Generalized Cross-Correlation Method -- 9.6. Adaptive Eigenvalue Decomposition Algorithm -- 9.7. Multichannel Cross-Correlation Algorithm -- 9.7.1. Forward Spatial Linear Prediction -- 9.7.2. Backward Spatial Linear Prediction -- 9.7.3. Spatial Linear Interpolation -- 9.7.4. Time Delay Estimation Using Spatial Linear Prediction -- 9.7.5. Spatial Correlation Matrix and Its Properties -- 9.7.6. Multichannel Cross-Correlation Coefficient -- 9.7.7. Time Delay Estimation Using MCCC -- 9.8. Adaptive Multichannel Time Delay Estimation -- 9.9. Acoustic Source Localization -- 9.10. Measurement Model and Cramer-Rao Lower Bound -- 9.11. Algorithm Overview -- 9.12. Maximum Likelihood Estimator -- 9.13. Least-Squares Estimators -- 9.13.1. Least-Squares Error Criteria -- 9.13.2. Spherical Intersection (SX) Estimator -- 9.13.3. Spherical Interpolation (SI) Estimator -- 9.13.4. Linear-Correction Least-Squares Estimator -- 9.14. Example System Implementation -- 9.15. Summary -- 10. Speech Enhancement and Noise Reduction -- 10.1. Introduction -- 10.2. Noise-Reduction and Speech-Distortion Measures -- 10.2.1. Noise-Reduction Factor and Noise-Reduction Gain Function -- 10.2.2. Speech-Distortion Index and Attenuation Frequency Distortion -- 10.2.3. Signal-to-Noise Ratio -- 10.2.4. Log-Spectral Distance -- 10.2.5. Itakura Distance -- 10.2.6. Itakura-Saito Distance -- 10.2.7. Mean Opinion Score -- 10.3. Single-Channel Noise-Reduction Algorithms: a Brief Overview -- 10.4. Time-Domain Wiener Filter -- 10.4.1. Estimation of the Clean Speech Samples -- 10.4.2. Estimation of the Noise Samples -- 10.4.3. Noise Reduction versus Speech Distortion -- 10.4.4. A Priori SNR versus a Posteriori SNR -- 10.4.5. Bounds for Noise Reduction and Speech Distortion -- 10.4.6. Particular Case: White Gaussian Noise -- 10.4.7. A Suboptimal Filter -- 10.5. Frequency-Domain Wiener Filter -- 10.5.1. Estimation of the Clean Speech Spectrum -- 10.5.2. A Priori SNR versus a Posteriori SNR -- 10.6. Noise Reduction Through Spectral Magnitude Restoration -- 10.7. Spectral Subtraction -- 10.7.1. Estimation of the Spectral Magnitude of the Clean Speech -- 10.7.2. Estimation of the Noise Spectrum -- 10.7.3. Relationship Between Spectral Subtraction and Wiener Filtering -- 10.7.4. Estimation of the Wiener Gain Filter -- 10.7.5. Simulations -- 10.8. Adaptive Noise Cancellation -- 10.8.1. Estimation of the Clean Speech -- 10.8.2. Ideal Noise Cancellation Performance -- 10.8.3. Signal Cancellation Problem -- 10.8.4. Simulations -- 10.9. Noise Reduction with a Microphone Array -- 10.9.1. Delay-and-Sum Algorithm --
• 10.9.2. Linearly Constrained Algorithms -- 10.10. Summary -- 11. Source Separation and Speech Dereverberation -- 11.1. Cocktail Party Effect -- 11.2. Source Separation -- 11.2.1. Microphone Array Beamforming -- 11.2.2. Independent Component Analysis and Blind Source Separation -- 11.3. A Synergistic Solution to Source Separation and Speech Dereverberation -- 11.4. Summary.

ISBN

• 0387276742 (alk. paper)
• 9780387276748 (alk. paper)
• 0387276769 (ebook)
• 9780387276762 (ebook)

LCCN

• 2006920463
• 9780387276748

OCLC

• ocm70412225
• SCSB-5306693

Owning Institutions

Columbia University Libraries