Time-Frequency Signal Analysis and Processing - 2nd Edition - ISBN: 9780123984999, 9780123985255

Time-Frequency Signal Analysis and Processing

2nd Edition

A Comprehensive Reference

Authors: Boualem Boashash
Hardcover ISBN: 9780123984999
eBook ISBN: 9780123985255
Imprint: Academic Press
Published Date: 2nd December 2015
Page Count: 1056
Tax/VAT will be calculated at check-out
Compatible Not compatible
VitalSource PC, Mac, iPhone & iPad Amazon Kindle eReader
ePub & PDF Apple & PC desktop. Mobile devices (Apple & Android) Amazon Kindle eReader
Mobi Amazon Kindle eReader Anything else

Institutional Access


Time-Frequency Signal Analysis and Processing (TFSAP) is a collection of theory, techniques and algorithms used for the analysis and processing of non-stationary signals, as found in a wide range of applications including telecommunications, radar, and biomedical engineering. This book gives the university researcher and R&D engineer insights into how to use TFSAP methods to develop and implement the engineering application systems they require.

New to this edition:

  • New sections on Efficient and Fast Algorithms; a "Getting Started" chapter enabling readers to start using the algorithms on simulated and real examples with the TFSAP toolbox, compare the results with the ones presented in the book and then insert the algorithms in their own applications and adapt them as needed.
  • Two new chapters and twenty three new sections, including updated references.
  • New topics including: efficient algorithms for optimal TFDs (with source code), the enhanced spectrogram, time-frequency modelling, more mathematical foundations, the relationships between QTFDs and Wavelet Transforms, new advanced applications such as cognitive radio, watermarking, noise reduction in the time-frequency domain, algorithms for Time-Frequency Image Processing, and Time-Frequency applications in neuroscience (new chapter).

Key Features

  • A comprehensive tutorial introduction to Time-Frequency Signal Analysis and Processing (TFSAP), accessible to anyone who has taken a first course in signals
  • Key advances in theory, methodology and algorithms, are concisely presented by some of the leading authorities on the respective topics
  • Applications written by leading researchers showing how to use TFSAP methods


University Researchers and R&D engineers in electrical and electronic engineering; signal, image and video processing engineers in universities and industry.

Table of Contents

<?xml version="1.0"?>

  • Dedication
  • Preface to the Second Edition
    • Acknowledgments
  • Preface to the First Edition
    • Acknowledgments
  • List of Contributors
  • Abbreviations
  • Book Standard Notations
  • Part I: A Time-Frequency Tutorial
    • Chapter I: The Time-Frequency Approach: Essence and Terminology
      • Introduction and Overview
      • I.1 Traditional Signal Representations
      • I.2 Joint Time-Frequency Representations
      • I.3 An Overview of Remaining Chapters and Key (t,f) Issues
      • I.4 Conclusions and Advice to the Reader
      • I.5 Additional Exercises
    • Chapter 1: Time-Frequency and Instantaneous Frequency Concepts
      • Abstract
      • Introduction and Overview
      • 1.1 The Benefits of Time-Frequency Distributions (TFDs)
      • 1.2 Signal Formulations and Characteristics in the (t,f) Domain
      • 1.3 Instantaneous Frequency (IF) and Spectral Delay (SD)
      • 1.4 Defining Amplitude, Phase, and IF Using the Analytic Signal
      • 1.5 Summary and Discussion
      • 1.6 Additional Exercises
    • Chapter 2: Heuristic Formulation of Time-Frequency Distributions0
      • Abstract
      • Introduction and Overview
      • 2.1 Method 1: The Wigner-Ville Distribution
      • 2.2 Method 2: Time-Varying Power Spectral Density
      • 2.3 Method 3: Windowed FT and Spectrogram
      • 2.4 Method 4: Filtered Function of Time
      • 2.5 Method 5: Instantaneous Power Spectra
      • 2.6 Method 6: Energy Density
      • 2.7 Relationship Between TFDs Using Time-Lag Kernels
      • 2.8 Summary and Discussion
      • 2.9 Additional Exercises
    • Chapter 3: Theory and Design of High-Resolution Quadratic TFDs0
      • Abstract
      • Introduction and Overview
      • 3.1 The Wigner-Ville Distribution
      • 3.2 Formulations of Quadratic TFDs (QTFDs)
      • 3.3 Properties of Quadratic TFDs (QTFDs)
      • 3.4 Positivity of QTFDs: Examples and Conditions
      • 3.5 QTFDs, Ambiguity Function, and Radar
      • 3.6 Concluding the Tutorial
      • 3.7 Additional Exercises
  • Part II: Fundamental Principles of TFSAP
    • Chapter 4: Advanced Time-Frequency Signal and System Analysis
      • Abstract
      • Introduction and Overview
      • 4.1 Relationships Between Quadratic TFDs and Time-Scale Representations0
      • 4.2 Cross-Terms and Localization in Quadratic Time-Frequency Distributions0
      • 4.3 The Covariance Theory of Time-Frequency Analysis0
      • 4.4 Uncertainty in Time-Frequency Analysis0
      • 4.5 Generalized TFRs via Unitary Transforms0
      • 4.6 Measurement Concepts in the Time-Frequency Plane0
      • 4.7 Time-Frequency Transfer Function Calculus of Linear Time-Varying Systems0
      • 4.8 Wigner Distribution and Fractional Fourier Transform1
      • 4.9 A Time-Frequency Perspective on Systems: From SISO to MIMO0
      • 4.10 Teager-Kaiser Energy Operators in Time-Frequency Analysis0
      • 4.11 Gabor Spectrogram, WVD, and Energy Atoms
      • 4.12 Emperical Mode Decomposition and Hilbert Spectrum
    • Chapter 5: Advanced Design and Specifications of TFDs
      • Abstract
      • Introduction and Overview
      • 5.1 Ambiguity Functions1
      • 5.2 Reduced Interference Time-Frequency Distributions1
      • 5.3 Adaptive Time-Frequency Analysis1
      • 5.4 Polynomial Wigner-Ville Distributions1
      • 5.5 Design of Polynomial TFDs, Time-Varying Higher Order Spectra, and Complex-Lag TFDs0
      • 5.6 Time-Frequency Representations Covariant to Spectral Delay Shifts0
      • 5.7 Design of High-Resolution Quadratic TFDs With Separable Kernels1
      • 5.8 Fractional Fourier Transform and Generalized-Marginal TFDs1
      • 5.9 Design of High-Resolution Quadratic TFDs With Multidirectional Kernels1
      • 5.10 Adaptive Directional Time-Frequency Distributions1
      • 5.11 The Modified S-Transform Applied to Time-Frequency Phase Synchrony1
      • 5.12 Kernel Conditions for the Positivity of Quadratic TFDs
  • Part III: Time-Frequency Methods
    • Chapter 6: Advanced Implementation and Realization of TFDs
      • Abstract
      • Introduction and Overview
      • 6.1 Discrete Time-Frequency Distributions0
      • 6.2 Quadratic and Higher Order Time-Frequency Analysis Based on the STFT0
      • 6.3 Gabor’s Signal Expansion for a Nonorthogonal Sampling Geometry0
      • 6.4 Spectrogram Decompositions of Time-Frequency Distributions0
      • 6.5 Computation of Discrete Quadratic TFDs0
      • 6.6 Memory-Efficient Algorithms for Quadratic TFDs
    • Chapter 7: Measures, Performance Assessment, and Enhancement of TFDs
      • Abstract
      • Introduction and Overview
      • 7.1 TFD Design Based on the Affine Group0
      • 7.2 Time-Frequency Reassignment0
      • 7.3 Measuring Time-Frequency Distributions Concentration0
      • 7.4 Resolution Performance Assessment for Comparing and Selecting Quadratic TFDs0
      • 7.5 Postprocessing in the (t,f) Domain: Methods and Performance Comparison
      • 7.6 Time-Frequency Compressive Sensing
      • 7.7 Signal Complexity Estimation Using Time-Frequency Short-Term Entropy
      • 7.8 Time-Frequency Analysis Using Neural Networks
      • 7.9 Defining Joint-Domain Representations via Discrete-Domain Frames
    • Chapter 8: Multisensor, Multichannel, and Time-Space Processing
      • Abstract
      • Introduction and Overview
      • 8.1 Multisensor Time-Frequency Analysis and Processing: Methods for Multichannel Nonstationary Data0
      • 8.2 Spatial Time-Frequency Distributions0
      • 8.3 Quadratic Detection in Arrays Using TFDs0
      • 8.4 Blind Source Separation Using Time-Frequency Distributions0
      • 8.5 Underdetermined Blind Source Separation for FM-Like Signals
      • 8.6 Audio Source Localization and Separation Using Time-Frequency Representations
      • 8.7 Implementation and Code of STFDs-Based Source Separation Algorithms
  • Part IV: TF Statistical Techniques
    • Chapter 9: Noise Analysis and Random Processes in the (t,f) Domain
      • Abstract
      • Introduction and Overview
      • 9.1 Analysis of Noise in Time-Frequency Distributions0
      • 9.2 Statistical Processing of Dispersive Systems and Signals0
      • 9.3 Robust Time-Frequency Distributions0
      • 9.4 Time-Varying Power Spectra of Nonstationary Random Processes0
      • 9.5 Time-Frequency Characterization of Random Time-Varying Channels0
      • 9.6 Characterization of Cyclostationary Signals and Their Generalizations0
    • Chapter 10: Instantaneous Frequency Estimation and Localization
      • Abstract
      • Introduction and Overview
      • 10.1 Iterative Instantaneous Frequency Estimation for Random Signals1
      • 10.2 Adaptive Instantaneous Frequency Estimation Using TFDs0
      • 10.3 IF Estimation For Multicomponent Signals0
      • 10.4 Parameter Estimation for Polynomial FM Signals in Additive Gaussian Noise0
      • 10.5 IF Estimation of FM Signals in Multiplicative Noise1
      • 10.6 Component Extraction from TFDs for Multicomponent Signal IF Estimation0
      • 10.7 Instantaneous Frequency and Polynomial Phase Parameter Estimation Using Linear Time-Frequency Representations0
      • 10.8 Sequential Bayesian Estimation of Instantaneous Frequencies
      • 10.9 Instantaneous frequency estimation using the Viterbi algorithm
    • Chapter 11: Time-Frequency Synthesis and Filtering
      • Abstract
      • Introduction and Overview
      • 11.1 Linear Time-Frequency Filters0
      • 11.2 Time-Varying Filtering Using the STFT and GABOR Expansion0
      • 11.3 Time-Frequency Filtering of Speech Signals in Hands-Free Telephone Systems0
      • 11.4 Signal Denoising by Time-Frequency Peak Filtering0
      • 11.5 Subspace Noise Filtering Using Time-Frequency Distributions0
      • 11.6 Evaluation of Time-Frequency Denoising Algorithms for Speech Enhancement0
    • Chapter 12: Detection, Classification, and Estimation in the (t,f) Domain
      • Abstract
      • Introduction and Overview
      • 12.1 Optimal Time-Frequency Detectors0
      • 12.2 Time-Frequency Signal Analysis and Classification Using Matching Pursuits0
      • 12.3 System Identification Using Time-Frequency Filtering0
      • 12.4 Time-Frequency Methods for Signal Estimation and Detection
      • 12.5 A General Approach to Time-Frequency-Matched Filtering
      • 12.6 Defining Time-Frequency Image Features by Extension from Frequency Domain or Time Domain
  • Part V: Engineering Applications
    • Chapter 13: Time-Frequency Methods in Communications
      • Abstract
      • Introduction and Overview
      • 13.1 Time-Frequency Interference Mitigation in Spread Spectrum Communication Systems1
      • 13.2 Communication Over Linear Dispersive Channels: A Time-Frequency Perspective1
      • 13.3 Eigenfunctions of Underspread Linear Time-Varying Systems1
      • 13.4 Fractional Autocorrelation For Detection in Communications1
      • 13.5 Time-Frequency Estimation of Radio-Signal Modulation Parameters
    • Chapter 14: Time-Frequency Methods in Radar, Sonar, and Acoustics
      • Abstract
      • Introduction and Overview
      • 14.1 Time-Frequency Analysis of Helicopter Doppler Radar Data1
      • 14.2 Time-Frequency Motion Compensation Algorithms for ISAR Imaging1
      • 14.3 Flight Parameter Estimation Using Doppler and Lloyd’S Mirror Effects0
      • 14.4 High-Frequency Radar Measurements of a Ballistic Missile Using the WVD0
      • 14.5 Time-Frequency Sonar Processing0
      • 14.6 Sparse Time-Frequency Distributions Applied to Geophysics
      • 14.7 Audio Speech and Underwater Signals Time-Frequency Characteristic Enhancement
    • Chapter 15: Time-Frequency Diagnosis, Condition Monitoring, and Fault Detection
      • Abstract
      • Introduction and Overview
      • 15.1 Time-Frequency Analysis of Electric Power Disturbances0
      • 15.2 Combustion Diagnosis by TF Analysis of Car Engine Signals0
      • 15.3 Power Class Time-Frequency Representations and Their Applications0
      • 15.4 The 2D Wigner-Ville Distribution Applied To Image Distortion Estimation0
      • 15.5 Extracting Time-Frequency Features from PCG Signals for Medical Diagnosis0
      • 15.6 Diagnosis and Condition Monitoring Using Time-Frequency Pattern Recognition and Machine Learning
      • 15.7 Condition Monitoring of Assets Using Time-Frequency Methods
    • Chapter 16: Time-Frequency Methodologies in Neurosciences
      • Abstract
      • Introduction and Overview
      • 16.1 Time-Frequency Diagnosis of Abnormalities in Newborn Physiological Signals1
      • 16.2 Time-Frequency Modeling of Nonstationary Signals for Newborn EEGs1
      • 16.3 Time-Frequency Features for Nonstationary Signal Classification with Illustration on Newborn EEG Burst-Suppression Detection1
      • 16.4 Time-Varying Analysis of Brain Networks1
      • 16.5 Time-Frequency Analysis and EEG Noise Reduction Using Empirical Mode Decomposition1
      • 16.6 Time-Frequency Methodologies for Assessment of Biosignals in Neurosciences1
    • Chapter 17: Getting Started with a Practical and Efficient Time-Frequency Toolbox TFSAP-7.0
      • Abstract
      • Introduction and Overview
      • 17.1 Introduction and (t,f) Toolbox Description
      • 17.2 Technical Content of the (t,f) Toolbox TFSAP 7.0
      • 17.3 User’s Guide for (t,f) Toolbox TFSAP-7.0
      • 17.4 A Brief (t,f) Tutorial using the (t,f) Toolbox
      • 17.5 Expanding and Personalizing the TFSAP Toolbox
      • 17.6 Summary and Conclusions
  • Additional Notes and Further Reading
  • Index


No. of pages:
© Academic Press 2016
Academic Press
eBook ISBN:
Hardcover ISBN:

About the Author

Boualem Boashash

Boualem Boashash (IEEE Fellow '99') is a Scholar, Professor and Senior Academic with experience in 5 leading Universities in France and Australia and 2 universities in the Middle-East. He has published over 500 technical publications, including over 100 journal papers, 3 books and 3 text-books covering Engineering, Applied Mathematics and Medicine. He was an early pioneer of the field of Time–Frequency Signal Processing and he is currently working on the further development of time-frequency theory and medical applications covering mental health and neurosciences with focus on newborn EEG analysis as well as ECG, HRV and fetal movements for improving health outcomes. Among many initiatives, he founded ISSPA, a leading conference since 1985 and its sister workshop WOSSPA. After founding a leading research group at The University of Queensland, he became the Foundation Professor and Director of the Signal Processing Research Centre at the Queensland University of Technology, Brisbane, Australia (1991-2005). He then became the Dean of Engineering at the University of Sharjah, United Arab Emirates (2006-2009) then Associate Dean, Academic at Qatar University and finally a Research Professor. In addition to the teaching, research and management experience, he also has 3 years industrial experience with Elf-Aquitaine in France at the beginning of his career. He is currently Professor at Qatar University, Department of Electrical Engineering, and the leader of a Biomedical Signal Processing group at the School of Medicine, University of Queensland, Brisbane, Australia. He also developed the first software package for time-frequency signal analysis and processing (TFSAP) regularly updated with his co-workers and used by hundreds of researchers around the world. His work has been cited over 10,000 times. Professor Boashash was a member of ICASSP board, associate editor for the IEEE transactions on signal processing and he is currently a member of the Board of the Elsevier journal Digital Signal Processing.

Affiliations and Expertise

Qatar University, Doha, Qatar, and University of Queensland, Brisbane, Australia