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(R) Code and Speech/Noise Databases Index
· · · · · · (收起)