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Speech Enhancement

Theory and Practice

By Philipos C. Loizou

Published June 7th 2007 by CRC Press – 632 pages

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  • Hardback: 978-0-8493-5032-0: $115.95 Add to Cart
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Description

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 nora.konopka@taylorandfrancis.com, providing proof of purchase.

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

Name: Speech Enhancement: Theory and Practice (Hardback) – CRC Press 
Description: By Philipos C. Loizou. 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...
Categories: Digital Signal Processing, Digital & Wireless Communication