Includes bibliographical references and index.

Introduction
Understanding the Enemy: Noise
Classes of Speech Enhancement Algorithms
Book Organization
References

Part I Fundamentals

Discrete-Time Signal Processing and Short-Time Fourier Analysis
Discrete-Time Signals
Linear Time-Invariant Discrete-Time Systems
z-Transform
Discrete-Time Fourier Transform
Short-Time Fourier Transform
Spectrographic Analysis of Speech Signals
Summary
References

Speech Production and Perception
Speech Signal
Speech Production Process
Engineering Model of Speech Production
Classes of Speech Sounds
Acoustic Cues in Speech Perception
Summary
References

Noise Compensation by Human Listeners
Intelligibility of Speech in Multiple-Talker Conditions
Acoustic Properties of Speech Contributing to Robustness
Perceptual Strategies for Listening in Noise
Summary
References

Part II Algorithms

Spectral-Subtractive Algorithms
Basic Principles of Spectral Subtraction
Geometric View of Spectral Subtraction
Shortcomings of the Spectral Subtraction Method
Spectral Subtraction Using Oversubtraction
Nonlinear Spectral Subtraction
Multiband Spectral Subtraction
MMSE Spectral Subtraction Algorithm
Extended Spectral Subtraction
Spectral Subtraction Using Adaptive Gain Averaging
Selective Spectral Subtraction
Spectral Subtraction Based on Perceptual Properties
Performance of Spectral Subtraction Algorithms
Summary
References

Wiener Filtering
Introduction to Wiener Filter Theory
Wiener Filters in the Time Domain
Wiener Filters in the Frequency Domain
Wiener Filters and Linear Prediction
Wiener Filters for Noise Reduction
Iterative Wiener Filtering
Imposing Constraints on Iterative Wiener Filtering
Constrained Iterative Wiener Filtering
Constrained Wiener Filtering
Estimating the Wiener Gain Function
Incorporating Psychoacoustic Constraints in Wiener Filtering
Codebook-Driven Wiener Filtering
Audible Noise Suppression Algorithm
Summary
References

Statistical-Model-Based Methods
Maximum-Likelihood Estimators
Bayesian Estimators
MMSE Estimator
Improvements to the Decision-Directed Approach
Implementation and Evaluation of the MMSE Estimator
Elimination of Musical Noise
Log-MMSE Estimator
MMSE Estimation of the pth-Power Spectrum
MMSE Estimators Based on Non-Gaussian Distributions
Maximum A Posteriori (Map) Estimators
General Bayesian Estimators
Perceptually Motivated Bayesian Estimators
Incorporating Speech Absence Probability in Speech Enhancement
Methods for Estimating the A Priori Probability of Speech Absence
Summary
References

Subspace Algorithms
Introduction
Using SVD for Noise Reduction: Theory
SVD-Based Algorithms: White Noise
SVD-Based Algorithms: Colored Noise
SVD-Based Methods: A Unified View
EVD-Based Methods: White Noise
EVD-Based Methods: Colored Noise
EVD-Based Methods: A Unified View
Perceptually Motivated Subspace Algorithms
Subspace-Tracking Algorithms
Summary
References

Noise-Estimation Algorithms
Voice Activity Detection vs. Noise Estimation
Introduction to Noise-Estimation Algorithms
Minimal-Tracking Algorithms
Time-Recursive Averaging Algorithms for Noise Estimation
Histogram-Based Techniques
Other Noise-Estimation Algorithms
Objective Comparison of Noise-Estimation Algorithms
Summary
References

Part III Evaluation

Evaluating Performance of Speech Enhancement Algorithms
Quality vs. Intelligibility
Evaluating Intelligibility of Processed Speech
Evaluating Quality of Processed Speech
Evaluating Reliability of Quality Judgments: Recommended Practice
Summary
References

Objective Quality and Intelligibility Measures
Objective Quality Measures
Evaluation of Objective Quality Measures
Quality Measures: Summary of Findings and Future Directions
Speech Intelligibility Measures
Evaluation of Intelligibility Measures
Intelligibility Measures: Summary of Findings and Future Directions
Summary
References

Comparison of Speech Enhancement Algorithms
NOIZEUS: A Noisy Speech Corpus for Quality Evaluation of Speech Enhancement Algorithms
Comparison of Enhancement Algorithms: Speech Quality
Comparison of Enhancement Algorithms: Speech Intelligibility
Summary
References

Part IV Future Steps

Algorithms That Can Improve Speech Intelligibility
Reasons for the Absence of Intelligibility Improvement with Existing Noise-Reduction Algorithms
Algorithms Based on Channel Selection: A Different Paradigm for Noise Reduction
Channel-Selection Criteria
Intelligibility Evaluation of Channel-Selection-Based Algorithms: Ideal Conditions
Implementation of Channel-Selection-Based Algorithms in Realistic Conditions
Evaluating Binary Mask Estimation Algorithms
Channel Selection and Auditory Scene Analysis
Summary
References

Appendices
Appendix A: Special Functions and Integrals
Appendix B: Derivation of the MMSE Estimator
Appendix C: MATLAB® Code and Speech/Noise Databases

Index