Speech Enhancement: Theory and Practice available in Hardcover
- Pub. Date:
- Taylor & Francis
The first book to provide comprehensive and up-to-date coverage of all major speech enhancement algorithms proposed in the last two decades, Speech Enhancement: Theory and Practice is a valuable resource for experts and newcomers in the field. The book covers traditional speech enhancement algorithms, such as spectral subtraction and Wiener filtering algorithms as well as state-of-the-art algorithms including minimum mean-squared error algorithms that incorporate signal-presence uncertainty and subspace algorithms that incorporate psychoacoustic models. The coverage includes objective and subjective measures used to evaluate speech quality and intelligibility.
Divided into three parts, the book presents the digital-signal processing and speech signal fundamentals needed to understand speech enhancement algorithms, the various classes of speech enhancement algorithms proposed over the last two decades, and the methods and measures used to evaluate the performance of speech enhancement algorithms. The text is supplemented with examples and figures designed to help readers understand the theory. MATLAB® implementations of all major speech enhancement algorithms and a speech database that can be used for evaluation of noise reduction algorithms are available for download on the book's description page at the CRC Press website.
Providing clear and concise coverage of the subject, the author brings together a large body of knowledge about how human listeners compensate for acoustic noise when in noisy environments. This book is a valuable resource not only for engineers who want to implement the latest speech enhancement algorithms but also for speech practitioners who want to incorporate some of these algorithms into hearing aid applications for speech intelligibility and/or quality improvement.
A download is available for those that purchase this book and can be obtained by contacting firstname.lastname@example.org, providing proof of purchase.
Table of Contents
Introduction Understanding the Enemy: Noise Classes of Speech Enhancement Algorithms Book Organization References
FUNDAMENTALS DISCRETE-TIME SIGNAL PROCESSING AND SHORT-TIME FOURIER ANALYSIS Discrete-Time Signals Linear Time-Invariant Discrete-Time Systems The z-Transform Discrete-Time Fourier Transform Short-Time Fourier Transform Spectrographic Analysis of Speech Signals Summary References
SPEECH PRODUCTION AND PERCEPTION The Speech Signal The 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
ALGORITHMS SPECTRAL-SUBTRACTIVE ALGORITHMS Basic Principles of Spectral Subtraction A 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 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
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 Objective Quality Measures Nonintrusive Objective Quality Measures Figures of Merit of Objective Quality Measures Challenges and Future Directions in Objective Quality Evaluation 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 Comparison of Objective Measures for Quality Evaluation Summary References
Appendix A: Derivation of the MMSE Estimator Appendix B: Special Functions and Integrals Appendix C: Speech Databases and MATLAB Code Index