Speech Enhancement: Theory and Practice

Speech Enhancement: Theory and Practice

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By (author) Philipos C. Loizou

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  • Publisher: CRC Press Inc
  • Format: Hardback | 711 pages
  • Dimensions: 160mm x 234mm x 38mm | 1,111g
  • Publication date: 17 April 2013
  • Publication City/Country: Bosa Roca
  • ISBN 10: 1466504218
  • ISBN 13: 9781466504219
  • Edition: 2, Revised
  • Edition statement: 2nd Revised edition
  • Illustrations note: 207 black & white illustrations, 24 black & white tables
  • Sales rank: 805,638

Product description

With the proliferation of mobile devices and hearing devices, including hearing aids and cochlear implants, there is a growing and pressing need to design algorithms that can improve speech intelligibility without sacrificing quality. Responding to this need, Speech Enhancement: Theory and Practice, Second Edition introduces readers to the basic problems of speech enhancement and the various algorithms proposed to solve these problems. Updated and expanded, this second edition of the bestselling textbook broadens its scope to include evaluation measures and enhancement algorithms aimed at improving speech intelligibility. Fundamentals, Algorithms, Evaluation, and Future Steps Organized into four parts, the book begins with a review of the fundamentals needed to understand and design better speech enhancement algorithms. The second part describes all the major enhancement algorithms and, because these require an estimate of the noise spectrum, also covers noise estimation algorithms. The third part of the book looks at the measures used to assess the performance, in terms of speech quality and intelligibility, of speech enhancement methods. It also evaluates and compares several of the algorithms. The fourth part presents binary mask algorithms for improving speech intelligibility under ideal conditions. In addition, it suggests steps that can be taken to realize the full potential of these algorithms under realistic conditions. What's New in This Edition Updates in every chapter A new chapter on objective speech intelligibility measures A new chapter on algorithms for improving speech intelligibility Real-world noise recordings (on accompanying CD) MATLAB(R) code for the implementation of intelligibility measures (on accompanying CD) MATLAB and C/C++ code for the implementation of algorithms to improve speech intelligibility (on accompanying CD) Valuable Insights from a Pioneer in Speech Enhancement Clear and concise, this book explores how human listeners compensate for acoustic noise in noisy environments. Written by a pioneer in speech enhancement and noise reduction in cochlear implants, it is an essential resource for anyone who wants to implement or incorporate the latest speech enhancement algorithms to improve the quality and intelligibility of speech degraded by noise. Includes a CD with Code and Recordings The accompanying CD provides MATLAB implementations of representative speech enhancement algorithms as well as speech and noise databases for the evaluation of enhancement algorithms.

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Author information

Philipos C. Loizou earned his bachelor's, master's, and doctorate degrees in electrical engineering from Arizona State University in Tempe. A pioneer in the field of speech enhancement and noise reduction in cochlear implants, Dr. Loizou was one of the first to develop specific enhancement algorithms that directly improve intelligibility. He was a postdoctoral fellow in the Department of Speech and Hearing Science at Arizona State University, an assistant professor at the University of Arkansas in Little Rock, and Cecil and Ida Green Professor in the Department of Electrical Engineering at the University of Texas at Dallas. Dr. Loizou was a fellow of the Acoustical Society of America. He was an associate editor of the International Journal of Audiology (2010-2012), IEEE Transactions on Biomedical Engineering (2009-2011), IEEE Transactions on Speech and Audio Processing (1999-2002), and IEEE Signal Processing Letters (2006-2009) and a member of the Speech Technical Committee (2008-2010) of the IEEE Signal Processing Society. He authored or coauthored numerous publications, including three textbooks. For more information, see Dr. Loizou's profile at the University of Texas at Dallas. Watch a video of Dr. Loizou talking about technology that would allow cochlear implant users to easily adjust settings on their hearing devices through a smartphone.

Review quote

"... by far the most comprehensive treatment of speech enhancement available. All the most important techniques in the broad field of speech enhancement are covered, yet the author at the same time manages to treat each topic in great detail. ... The algorithms are complex, but Loizou's exposition is outstanding. The second edition brings the material right up to date, covering recent significant breakthroughs in binary masking algorithms ... . One of the great strengths of this text is the availability of code, allowing readers to better understand, deploy, and extend existing algorithms for speech enhancement. ... This volume is in reality far more than a textbook on speech enhancement. It is also one of the most important works on the effect of noise on speech perception, and as such will make a huge contribution to the education of the next generation of auditory scientists, and feed technological developments in all aspects of speech communication, particularly for individuals with hearing impairment." -Prof. Martin Cooke, Ikerbasque and University of the Basque Country, Vitoria, Spain "This textbook offers outstanding reference material for teaching the clinical application of spectral enhancement to the audiology community. Dr. Loizou offers the reader tremendous insight into the fundamentals of digital signal processing, speech production and perception, and the characteristics of various noise sources. ... The textbook is essential for engineers, audiologists, and other professionals who seek to improve the listener's ability to hear a target signal against a background filled with competing noise using the spectral enhancement technique." -Amyn M. Amlani, Ph.D., University of North Texas, Denton, USA "... a highly informative presentation of the fundamentals, seminal and current algorithms, evaluation metrics, and future work that is desirable for any new or experienced students and researchers in the exciting area of speech enhancement. ... I greatly appreciate the excellent organization of dividing the book into Fundamentals, Algorithms, Evaluation, and Future Steps, which can allow instructors and researchers to quickly decide on the material they want to teach their students, or learn or review themselves ... Dr. Loizou takes students and researchers with a range of experiences on an amazing journey through the exciting field of speech enhancement." -Marek Trawicki, Marquette University, Wauwatosa, Wisconsin, USA "The first edition of this book established itself as the best reference for single-channel speech enhancement. Amazingly, this new edition is even better, and could be the most authoritative work in the area of modern single-channel techniques for speech enhancement to date. ... This is a unique book, combining both thorough theoretical developments and practical implementations. I highly recommend it to those interested in speech enhancement, as well as applied signal processing." -Association of Computing Machinery (ACM) Computing Reviews, July 2013 Reviewer: Vladimir Botchev, Analog Devices, Wilmington, Massachusetts, USA

Table of contents

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