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Academic Press Library in Signal Processing

Book Description

This first volume, edited and authored by world leading experts, gives a review of the principles, methods and techniques of important and emerging research topics and technologies in machine learning and advanced signal processing theory.

With this reference source you will:

  • Quickly grasp a new area of research 
  • Understand the underlying principles of a topic and its application
  • Ascertain how a topic relates to other areas and learn of the research issues yet to be resolved


  • Quick tutorial reviews of important and emerging topics of research in machine learning
  • Presents core principles in signal processing theory and shows their applications
  • Reference content on core principles, technologies, algorithms and applications
  • Comprehensive references to journal articles and other literature on which to build further, more specific and detailed knowledge
  • Edited by leading people in the field who, through their reputation, have been able to commission experts to write on a particular topic

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Introduction
    1. Signal Processing at Your Fingertips!
  6. About the Editors
  7. Section Editors
    1. Section 1
    2. Section 2
  8. Authors Biography
    1. Chapter 1
    2. Chapter 2
    3. Chapter 3
    4. Chapter 4
    5. Chapter 5
    6. Chapter 6
    7. Chapter 7
    8. Chapter 8
    9. Chapter 9
    10. Chapter 10
    11. Chapter 11
    12. Chapter 12
    13. Chapter 14
    14. Chapter 15
    15. Chapter 16
    16. Chapter 17
    17. Chapter 18
    18. Chapter 19
    19. Chapter 20
    20. Chapter 21
    21. Chapter 22
    22. Chapter 24
    23. Chapter 25
    24. Chapter 26
  9. Section 1: SIGNAL PROCESSING THEORY
    1. Chapter 1. Introduction to Signal Processing Theory
      1. Abstract
      2. 1.01.1 Introduction
      3. 1.01.2 Continuous-time signals and systems
      4. 1.01.3 Discrete-time signals and systems
      5. 1.01.4 Random signals and stochastic processes
      6. 1.01.5 Sampling and quantization
      7. 1.01.6 FIR and IIR filter design
      8. 1.01.7 Digital filter structures and implementations
      9. 1.01.8 Multirate signal processing
      10. 1.01.9 Filter banks and wavelets
      11. 1.01.10 Discrete multiscale and transforms
      12. 1.01.11 Frames
      13. 1.01.12 Parameter estimation
      14. 1.01.13 Adaptive filtering
      15. 1.01.14 Closing comments
      16. References
    2. Chapter 2. Continuous-Time Signals and Systems
      1. Abstract
      2. Nomenclature
      3. 1.02.1 Introduction
      4. 1.02.2 Continuous-time systems
      5. 1.02.3 Differential equations
      6. 1.02.4 Laplace transform: definition and properties
      7. 1.02.5 Transfer function and stability
      8. 1.02.6 Frequency response
      9. 1.02.7 The Fourier series and the Fourier transform
      10. 1.02.8 Conclusion and future trends
      11. Glossary
      12. 1.02.9 Relevant Websites:
      13. 1.02.10 Supplementary data
      14. 1.02.11 Supplementary data
      15. References
    3. Chapter 3. Discrete-Time Signals and Systems
      1. Abstract
      2. 1.03.1 Introduction
      3. 1.03.2 Discrete-time signals: sequences
      4. 1.03.3 Discrete-time systems
      5. 1.03.4 Linear time-invariant (LTI) systems
      6. 1.03.5 Discrete-time signals and systems with MATLAB
      7. 1.03.6 Conclusion
      8. References
    4. Chapter 4. Random Signals and Stochastic Processes
      1. Abstract
      2. Acknowledgements
      3. 1.04.1 Introduction
      4. 1.04.2 Probability
      5. 1.04.3 Random variable
      6. 1.04.4 Random process
      7. References
    5. Chapter 5. Sampling and Quantization
      1. Abstract
      2. 1.05.1 Introduction
      3. 1.05.2 Preliminaries
      4. 1.05.3 Sampling of deterministic signals
      5. 1.05.4 Sampling of stochastic processes
      6. 1.05.5 Nonuniform sampling and generalizations
      7. 1.05.6 Quantization
      8. 1.05.7 Oversampling techniques
      9. 1.05.8 Discrete-time modeling of mixed-signal systems
      10. References
    6. Chapter 6. Digital Filter Structures and Their Implementation
      1. Abstract
      2. 1.06.1 Introduction
      3. 1.06.2 Digital FIR filters
      4. 1.06.3 The analog approximation problem
      5. 1.06.4 Doubly resistively terminated lossless networks
      6. 1.06.5 Ladder structures
      7. 1.06.6 Lattice structures
      8. 1.06.7 Wave digital filters
      9. 1.06.8 Frequency response masking (FRM) structure
      10. 1.06.9 Computational properties of filter algorithms
      11. 1.06.10 Architecture
      12. 1.06.11 Arithmetic operations
      13. 1.06.12 Sum-of-products (SOP)
      14. 1.06.13 Power reduction techniques
      15. References
    7. Chapter 7. Multirate Signal Processing for Software Radio Architectures
      1. Abstract
      2. 1.07.1 Introduction
      3. 1.07.2 The Sampling process and the “Resampling” process
      4. 1.07.3 Digital filters
      5. 1.07.4 Windowing
      6. 1.07.5 Basics on multirate filters
      7. 1.07.6 From single channel down converter to standard down converter channelizer
      8. 1.07.7 Modifications of the standard down converter channelizer—M:2 down converter channelizer
      9. 1.07.8 Preliminaries on software defined radios
      10. 1.07.9 Proposed architectures for software radios
      11. 1.07.10 Closing comments
      12. Glossary
      13. References
    8. Chapter 8. Modern Transform Design for Practical Audio/Image/Video Coding Applications
      1. Abstract
      2. 1.8.1 Introduction
      3. 1.8.2 Background and fundamentals
      4. 1.8.3 Design strategy
      5. 1.8.4 Approximation approach via direct scaling
      6. 1.8.5 Approximation approach via structural design
      7. 1.8.6 Wavelet filters design via spectral factorization
      8. 1.8.7 Higher-order design approach via optimization
      9. 1.8.8 Conclusion
      10. References
    9. Chapter 9. Discrete Multi-Scale Transforms in Signal Processing
      1. Abstract
      2. 1.09.1 Introduction
      3. 1.09.2 Wavelets: a multiscale analysis tool
      4. 1.09.3 Curvelets and their applications
      5. 1.09.4 Contourlets and their applications
      6. 1.09.5 Shearlets and their applications
      7. A Appendix
      8. References
    10. Chapter 10. Frames in Signal Processing
      1. Abstract
      2. 1.10.1 Introduction
      3. 1.10.2 Basic concepts
      4. 1.10.3 Relevant definitions
      5. 1.10.4 Some computational remarks
      6. 1.10.5 Construction of frames from a prototype signal
      7. 1.10.6 Some remarks and highlights on applications
      8. 1.10.7 Conclusion
      9. References
    11. Chapter 11. Parametric Estimation
      1. Abstract
      2. 1.11.1 Introduction
      3. 1.11.2 Deterministic and stochastic signals
      4. 1.11.3 Parametric models for signals and systems
      5. References
    12. Chapter 12. Adaptive Filters
      1. Abstract
      2. Acknowledgment
      3. 1.12.1 Introduction
      4. 1.12.2 Optimum filtering
      5. 1.12.3 Stochastic algorithms
      6. 1.12.4 Statistical analysis
      7. 1.12.5 Extensions and current research
      8. 1.12.6 Supplementary data
      9. References
  10. Section 2: MACHINE LEARNING
    1. Chapter 13. Introduction to Machine Learning
      1. Abstract
      2. Acknowledgments
      3. 1.13.1 Scope and context
      4. 1.13.2 Contributions
      5. References
    2. Chapter 14. Learning Theory
      1. Abstract
      2. 1.14.1 Introduction
      3. 1.14.2 Probabilistic formulation of learning problems
      4. 1.14.3 Uniform convergence of empirical means
      5. 1.14.4 Model selection
      6. 1.14.5 Alternatives to uniform convergence
      7. 1.14.6 Computational aspects
      8. 1.14.7 Beyond the basic probabilistic framework
      9. 1.14.8 Conclusions and future trends
      10. Glossary
      11. Relevant websites
      12. References
    3. Chapter 15. Neural Networks
      1. Abstract
      2. 1.15.1 Introduction
      3. 1.15.2 Learning with single neurons
      4. 1.15.3 Recurrent neural networks
      5. 1.15.4 Learning by focussing on the generalization ability
      6. 1.15.5 Unsupervised learning
      7. 1.15.6 Applications
      8. 1.15.7 Open issues and problems
      9. 1.15.8 Implementation, code, and data sets
      10. 1.15.9 Conclusions and future trends
      11. Glossary
      12. References
    4. Chapter 16. Kernel Methods and Support Vector Machines
      1. Abstract
      2. Nomenclature
      3. Acknowledgment
      4. 1.16.1 Introduction
      5. 1.16.2 Foundations of kernel methods
      6. 1.16.3 Fundamental kernel methods
      7. 1.16.4 Computational issues of kernel methods
      8. 1.16.5 Multiple kernel learning
      9. 1.16.6 Applications
      10. 1.16.7 Open issues and problems
      11. Glossary
      12. References
    5. Chapter 17. Online Learning in Reproducing Kernel Hilbert Spaces
      1. Abstract
      2. Nomenclature
      3. 1.17.1 Introduction
      4. 1.17.2 Parameter estimation: The regression and classification tasks
      5. 1.17.3 Overfitting and regularization
      6. 1.17.4 Mapping a nonlinear to a linear task
      7. 1.17.5 Reproducing Kernel Hilbert spaces
      8. 1.17.6 Least squares learning algorithms
      9. 1.17.7 A convex analytic toolbox for online learning
      10. 1.17.8 Related work and applications
      11. 1.17.9 Conclusions
      12. Appendices
      13. B Proof of Proposition 60
      14. C Proof of convergence for Algorithm 61
      15. References
    6. Chapter 18. Introduction to Probabilistic Graphical Models
      1. Abstract
      2. Nomenclature
      3. Acknowledgments
      4. 1.18.1 Introduction
      5. 1.18.2 Preliminaries
      6. 1.18.3 Representations
      7. 1.18.4 Learning
      8. 1.18.5 Inference
      9. 1.18.6 Applications
      10. 1.18.7 Implementation/code
      11. 1.18.8 Data sets
      12. 1.18.9 Conclusion
      13. Glossary
      14. References
    7. Chapter 19. A Tutorial Introduction to Monte Carlo Methods, Markov Chain Monte Carlo and Particle Filtering
      1. Abstract
      2. 1.19.1 Introduction
      3. 1.19.2 The Monte Carlo principle
      4. 1.19.3 Basic techniques for simulating random variables
      5. 1.19.4 Markov Chain Monte Carlo
      6. 1.19.5 Sequential Monte Carlo
      7. 1.19.6 Advanced Monte Carlo methods
      8. 1.19.7 Open issues and problems
      9. 1.19.8 Further reading
      10. Glossary
      11. References
    8. Chapter 20. Clustering
      1. Abstract
      2. 1.20.1 Introduction
      3. 1.20.2 Clustering algorithms
      4. 1.20.3 Clustering validation
      5. 1.20.4 Applications
      6. 1.20.5 Open issues and problems
      7. 1.20.6 Conclusion
      8. Glossary
      9. References
    9. Chapter 21. Unsupervised Learning Algorithms and Latent Variable Models: PCA/SVD, CCA/PLS, ICA, NMF, etc.
      1. Abstract
      2. 1.21.1 Introduction and of problems statement
      3. 1.21.2 PCA/SVD and related problems
      4. 1.21.3 ICA and related problems
      5. 1.21.4 NMF and related problems
      6. 1.21.5 Future directions: constrained multi-block tensor factorizations and multilinear blind source separation
      7. 1.21.6 Summary
      8. References
    10. Chapter 22. Semi-Supervised Learning
      1. Abstract
      2. 1.22.1 Introduction
      3. 1.22.2 Semi-supervised learning algorithms
      4. 1.22.3 Semi-supervised learning for structured outputs
      5. 1.22.4 Large scale semi-supervised learning
      6. 1.22.5 Theoretical analysis overview
      7. 1.22.6 Challenges
      8. Glossary
      9. References
      10. Relevant websites
    11. Chapter 23. Sparsity-Aware Learning and Compressed Sensing: An Overview
      1. 1.23.1 Introduction
      2. 1.23.2 Parameter estimation
      3. 1.23.3 Searching for a norm
      4. 1.23.4 The least absolute shrinkage and selection operator (LASSO)
      5. 1.23.5 Sparse signal representation
      6. 1.23.6 In quest for the sparsest solution
      7. 1.23.7 Uniqueness of the minimizer
      8. 1.23.8 Equivalence of and minimizers: sufficiency conditions
      9. 1.23.9 Robust sparse signal recovery from noisy measurements
      10. 1.23.10 Compressed sensing: the glory of randomness
      11. 1.23.11 Sparsity-promoting algorithms
      12. 1.23.12 Variations on the sparsity-aware theme
      13. 1.23.13 Online time-adaptive sparsity-promoting algorithms
      14. 1.23.14 Learning sparse analysis models
      15. 1.23.15 A case study: time-frequency analysis
      16. 1.23.16 From sparse vectors to low rank matrices: a highlight
      17. 1.23.17 Conclusions
      18. Appendix
      19. References
    12. Chapter 24. Information Based Learning
      1. 1.24.1 Introduction
      2. 1.24.2 Information theoretic descriptors
      3. 1.24.3 Unifying information theoretic framework for machine learning
      4. 1.24.4 Nonparametric information estimators
      5. 1.24.5 Reproducing kernel Hilbert space framework for ITL
      6. 1.24.6 Information particle interaction for learning from samples
      7. 1.24.7 Illustrative examples
      8. 1.24.8 Conclusions and future trends
      9. References
    13. Chapter 25. A Tutorial on Model Selection
      1. Abstract
      2. 1.25.1 Introduction
      3. 1.25.2 Minimum distance estimation criteria
      4. 1.25.3 Bayesian approaches to model selection
      5. 1.25.4 Model selection by compression
      6. 1.25.5 Simulation
      7. References
    14. Chapter 26. Music Mining
      1. Abstract
      2. Acknowledgments
      3. 1.26.1 Introduction
      4. 1.26.2 Ground truth acquisition and evaluation
      5. 1.26.3 Audio feature extraction
      6. 1.26.4 Extracting context information about music
      7. 1.26.5 Similarity search
      8. 1.26.6 Classification
      9. 1.26.7 Tag annotation
      10. 1.26.8 Visualization
      11. 1.26.9 Advanced music mining
      12. 1.26.10 Software and datasets
      13. 1.26.11 Open problems and future trends
      14. 1.26.12 Further reading
      15. Glossary
      16. References
  11. Index