The authors offer a comprehensive guide to machine learning applied to signal processing and recognition problems, and then discuss real applications in domains such as speech processing and biomedical signal processing, with a focus on handling noise. This textbook is intended for advanced undergraduate and graduate students of computer science and engineering.
Prof. Michael M. Richter completed his PhD on mathematical logic at the University of Freiburg, and his Habilitation in mathematics at the University of Tübingen. He taught at the University of Texas at Austin and at RWTH Aachen, in addition to numerous visiting professorships. Most recently he held a chair in computer science at the University of Kaiserslautern, where he was also the founding scientific director of the DFKI (German Research Center for Artificial Intelligence). He is currently an adjunct professor at the University of Calgary. He has taught, researched, and published extensively in the areas of mathematical logic and artificial intelligence. Prof. Richter is one of the pioneers of case-based reasoning: he founded the leading European event on the subject, he led many of the key academic research projects, and he demonstrated the real-world viability of the approach with successful commercial products.
Dr. Sheuli Paul completed her PhD on a dynamic automatic noisy speech recognition system in Kaiserslautern. Her interests include speech recognition and signal processing.
Part I Realms of Signal Processing
1 Digital Signal Representation
1.1 Introduction
1.2 Numbers
1.2.1 Numbers and Numerals
1.2.2 Types of Numbers
1.3 Sampling and Reconstruction of Signals
1.3.1 Scalar Quantization
1.3.3 Signal-To-Noise Ratio
1.3.4 Transmission Rate
1.3.5 Nonuniform Quantizer
1.3.6 Companding
1.4 Data Representations
1.4.1 Fixed-Point Number Representations
1.4.2 Sign-Magnitude Format
1.4.3 One's-Complement Format
1.4.4 Two's-Complement Format
1.6 Fixed-Point Representations Based on Radix-Point
1.7 Dynamic Range
1.9 Background Information
1.10 Exercises
2 Signal Processing Background
2.1 Basic Concepts
2.2 Signals and Information
2.3 Signal Processing
ix
x Contents
2.4 Discrete Signal Representations
2.6 Parseval's Theorem
2.7 Gibbs Phenomenon
2.8 Wold Decomposition
2.9 State Space Signal Processing
2.10 Common Measurements
2.10.1 Convolution
2.10.2 Correlation
2.10.3 Auto Covariance
2.10.4 Coherence
2.10.6 Estimation and Detection
2.10.7 Central Limit Theorem
2.10.9 Machine Learning
2.10.10Exercises
3 Fundamentals of Signal Transformations
3.1 Transformation Methods
3.1.1 Laplace Transform
3.1.3 Fourier Series
3.1.4 Fourier Transform
3.1.5 Discrete Fourier Transform and Fast Fourier Transform
3.1.6 Zero Padding
3.1.7 Overlap-Add and Overlap-Save Convolution
Algorithms
3.1.8 Short Time Fourier Transform (STFT)
3.1.9 Wavelet Transform
3.1.10 Windowing Signal and the DCT Transforms
3.2 Analysis and Comparison of Transformations
3.3 Background Information
3.4 Exercises
4 Digital Filters
4.1 Introduction
4.1.2 Bilinear Transform
4.2 Windowing for Filtering
4.3 Allpass Filters
4.4 Lattice Filters
4.5 All-Zero Lattice Filter
4.6 Lattice Ladder Filters
Contents xi
4.7 Comb Filter
4.8 Notch Filter
4.10 Exercises
5 Estimation and Detection
5.2 Hypothesis Testing
5.2.1 Bayesian Hypothesis Testing
5.2.2 MAP Hypothesis Testing
5.3 Maximum Likelihood (ML) Hypothesis Testing
5.4 Standard Analysis Techniques
5.4.1 Best Linear Unbiased Estimator (BLUE)
5.4.2 Maximum Likelihood Estimator (MLE)
5.4.3 Least Squares Estimator (LSE)
5.4.4 Linear Minimum Mean Square Error Estimator
(LMMSE)
5.5 Exercises
6 Adaptive Signal Processing
6.1 Introduction
6.2 Parametric Signal Modeling
6.2.1 Parametric Estimation
6.4 Kalman Filter
6.4.1 Smoothing
6.5 Particle Filter
6.6 Fundamentals of Monte Carl
6.6.1 Importance Sampling (IS)
6.7 Non-Parametric Signal Modeling
6.8 Non-Parametric Estimation
6.8.1 Correlogram
6.8.2 Periodogram
6.10 Quadrature Mirror Filter Bank (QMF)
6.11 Background Information
7 Spectral Analysis
7.1 Introduction
7.3 Multivariate Signal Processing
7.3.1 Sub-band Coding and Subspace Analysis
7.5 Adaptive Beam Forming
xii Contents
7.6 Independent Component Analysis (ICA)
7.7 Principal Component Analysis (PCA)
7.8 Best Basis Algorithms
7.9 Background Information
7.10 Exercises
Part II Machine Learning and Recognition
8 General Learning
8.1 Introduction to Learning
8.2 The Learning Phases
8.2.1 Search and Utility
8.3.1 General Search Model
8.3.2 Preference relations
8.3.3 Different learning methods
8.3.4 Similarities
8.3.5 Learning to Recognize
8.4 Background Information
8.5 Exercises
9 Signal Processes, Learning, and Recognition
9.1 Learning
9.2 Bayesian Formalism
9.2.1 Dynamic Bayesian Theory
9.2.2 Recognition and Search
9.2.3 Influences
9.3 Subjectivity
9.5 Exercises
10 Stochastic Processes
10.2 Basic Concepts of Stochastic Processes
10.2.1 Markov Processes
10.2.3 HSM Topology
10.2.4 Learning Probabilities
10.2.5 Re-estimation
10.2.6 Redundancy
10.2.7 Data Preparation
10.2.8 Proper Redundancy Removal
10.3 Envelope Detection
10.3.1 Silence Threshold Selection
10.3.2 Pre-emphasis
10.4 Several Processes
10.4.1 Similarity
10.4.2 The Local-Global Principle
10.4.3 HSM Similarities
10.5 Conflict and Support
10.6 Examples and Applications
10.7 Predictions
10.8 Background Information
10.9 Exercises
11 Feature Extraction
11.1 Feature Extractions
11.2 Basic Techniques
11.3 Spectral Analysis and Feature Transformation
11.3.1 Parametric Feature Transformations and Cepstrum
11.3.3 Frame Energy
11.4 Linear Prediction Coe_cients (LPC)
11.5 Linear Prediction Cepstral Coe_cients (LPCC)
11.6 Adaptive Perceptual Local Trigonometric Transformation
(APLTT)
11.7 Search
11.7.1 General Search Model
11.8.1 Purpose
11.8.2 Linear Prediction
11.8.3 Mean Squared Error Minimization
11.8.4 Computation of Probability of an Observation Sequence
11.8.5 Forward and Backward Prediction
11.8.6 Forward-Backward Prediction
11.9 Background Information
11.10Exercises
12 Unsupervised Learning
12.1 Generalities
12.2 Clustering Principles
12.3 Cluster Analysis Methods
12.4.1 K-means
12.4.2 Vector Quantization (VQ)
12.4.3 Expectation Maximization (EM)
12.4.4 GMM Clustering
12.5 Background Information
12.6 Exercises
xiv Contents
13 Markov Model and Hidden Stochastic Model
13.1 Markov Process
13.2 Gaussian Mixture Model (GMM)
13.3 Advantages of using GMM
13.4 Linear Prediction Analysis
13.4.2 Yule-Walker Approach
13.4.3 Covariance Method
13.4.4 Comparison of Correlation and Covariance methods
13.5 The ULS Approach
13.6 Comparison of ULS and Covariance Methods
13.7 Forward Prediction
13.8 Backward Prediction
13.9 Forward-Backward Prediction
13.10Baum-Welch Algorithm
13.12Background Information
13.13Exercises
14 Fuzzy Logic and Rough Sets
14.1 Rough Sets
14.2 Fuzzy Sets
14.2.1 Basis Elements
14.2.2 Possibility and Necessity
14.3 Fuzzy Clustering
14.4 Fuzzy Probabilities
14.6 Exercises
15 Neural Networks
15.1.1 Neural Network Training
15.1.2 Neural Network Topology
15.2 Parallel Distributed Processing
15.2.1 Forward and Backward Uses
15.2.2 Learning
15.3 Applications to Signal Processing
15.4 Background Information
15.5 Exercises
Part III Real Aspects and Applications
16 Noisy Signals
16.1 Introduction
16.3 Sources of Noise
16.4 Noise Measurement
16.5 Weights and A-Weights
16.6 Signal to Noise Ratio (SNR)
16.7 Noise Measuring Filters and Evaluation
16.8 Types of noise
16.9 Origin of noises
16.10Box Plot Evaluation
16.11Individual noise types
16.11.2Mild
16.11.3Steady-unsteady Time varying Noise
16.11.4Strong Noise
16.12Solution to Strong Noise: Matched Filter
16.13Background Information
16.14Exercises
17 Reasoning Methods and Noise Removal
17.1 Generalities
17.2 Special Noise Removal Methods
17.2.1 Residual Noise
17.2.2 Mild Noise
17.2.3 Steady-Unsteady Noise
17.3 Poisson Distribution
17.3.1 Outliers and Shots
17.3.2 Underlying probability of Shots
17.4 Kalman Filter
17.4.1 Prediction Estimates
17.4.2 White noise Kalman filtering
17.4.3 Application of Kalman filter
17.5 Classification, Recognition and Learning
17.5.1 Summary of the used concepts
17.6 Principle Component Analysis (PCA)
17.7 Reasoning Methods
17.7.1 Case-Based Reasoning (CBR)
17.9 Exercises
xvi Contents
18 Audio Signals and Speech Recognition
18.1 Generalities of Speech
18.2 Categories of Speech Recognition
18.3 Automatic Speech Recognition
18.3.1 System Structure
18.4 Speech Production Model
18.5 Acoustics
18.6 Human Speech Production
18.6.1 The Human Speech Generation
18.6.2 Excitation
18.6.4 Unvoiced Speech
18.7 Silence Regions
18.8 Glottis
18.9 Lips
18.10Plosive Speech Source
18.11Vocal-Tract
18.13Formants
18.14Strong Noise
18.15Background Information
18.16Exercises
19 Noisy Speech
19.1 Introduction
19.2 Colored Noise
19.2.1 Additional types of Colored Noise
19.3 Poisson Processes and Shots
19.4 Matched Filters
19.5 Shot Noise
19.6 Background Information
20 Aspects Of Human Hearing
20.1 Human Ear
20.3 Critical Bands and Scales
20.3.1 Mel Scale
20.3.2 Bark Scale
20.3.3 Erb Scale
20.3.4 Greenwood Scale
20.4 Filter Banks
20.4.2 Auditory Filter Banks
20.4.3 Filter Banks
Contents xvii
20.4.4 Mel Critical Filter Bank
20.5 Psycho-acoustic Phenomena
20.5.1 Perceptual Measurement
20.5.2 Human Hearing and Perception
20.5.3 Sound Pressure Level (SPL)
20.5.4 Absolute Threshold of Hearing (ATH)
20.6 Perceptual Adaptation
20.7 Auditory System and Hearing Model
20.8 Auditory Masking and Masking Frequency
20.9 Perceptual Spectral Features
20.10Critical Band Analysis
20.11Equal Loudness Pre-emphasis
20.13Feature Transformation
20.14Filters and Human Ear
20.15Temporal Aspects
20.16Background Information
20.17Exercises
21 Speech Features
21.1 Generalities
21.2 Cost Functions
21.3 Special Feature Extractions
21.3.1 MFCC Features
21.3.2 Feature Transformation applying DCT
21.4 Background Information
22 Hidden Stochastic Model for Speech
22.1 General
22.3 Forward and Backward Predictions
22.3.1 Forward Algorithm
22.4 Forward-Backward Prediction
22.5 Burg Approach
22.6 Graph Search
22.6.1 Recognition Model with Search
22.7 Semantic Issues and Industrial Applications
22.8 Problems with Noise
22.9 Aspects of Music
22.10Music reception
22.11Background Information
xviii Contents
23 Different Speech Applications - Part A
23.1 Generalities
23.2 Example Applications
23.2.1 Experimental laboratory
23.2.2 Health care support (everyday actions)
23.2.3 Diagnostic support for persons with possible dementia
23.2.4 Noise
23.3 Background Information
23.4 Exercises
24 Different Speech Applications - Part B
24.1 Introduction
24.2 Discrete-Time Signals
24.3 Speech Processing
24.3.1 Framing
24.3.2 Pre-emphasis
24.3.4 Fourier Transform
24.3.5 Mel-Filtering
24.3.6 Mel-Frequency Cepstral Coeffcients
24.4 Speech Analysis and Sound Effects Laboratory (SASE_Lab)
24.5.1 Introduction
24.5.2 Wake-up-Word Paradigm
24.5.3 Wake-Up-Word: Definition
24.5.4 Wake-Up-Word System
24.5.5 Front-End of the Wake-Up-Word System
24.6 Conclusion
24.6.1 Wake-Up-Word: Tool Demo
24.6.2 Elevator Simulator
24.7 Background Information
24.9 Speech Analysis and Sound E_ects Laboratory (SASE_Lab)"
25 Biomedical Signals: ECG, EEG
25.1 ECG signals
25.1.1 Bioelectric Signals
25.1.2 Noise
25.2 EEG Signals
25.2.1 General properties
25.2.2 Signal types and properties
25.3 Neural Network use
25.4 Major Research Questions
25.5 Background Information
Contents xix
25.6 Exercises
26 Seismic Signals
26.1 Generalities
26.2 Sources of seismic signals
26.3 Intermediate elements
26.5 Major seismic problems
26.6 Noise
26.7 Background Information
26.8 Exercises
27 Radar Signals
27.1 Introduction
27.2 Radar Types and Applications
27.3 Doppler Equations, Ambiguity Function(AF) and Matched
Filter
27.4 Moving Target Detection
27.5 Applications and Discussions
27.6 Examples
27.8 Exercises
28 Visual Story Telling
28.1.1 Common Visualization Approaches
28.2 Analytics and Visualization
28.2.2 Visual Data Minin
28.3 Communication and Visualization
28.4 Background Information
28.5 Exercises
29 Digital Processes and Multimedia
29.1 Images
29.1.1 Digital Image Processing
29.1.2 Images as Matrices
29.1.3 Gray Scale Images
29.2 Spatial Filtering
29.2.1 Linear Filtering of Images
29.2.2 Separable Filters
29.3 Median Filtering
29.4 Color Equalization
29.4.2 Examples of Image Transformation Matrixes
xx Contents
29.5 Basic Image Statistics
29.6 Abstraction Levels of Images and its Representations
29.6.1 Lowest Level
29.6.2 Geometric Level
29.6.3 Domain Level
29.6.4 Segmentation
29.7 Background Information
30 Visualizations of Emergency Operation Centre
30.1 Introduction
30.2 Communications in Emergency Situations
30.3 Emergency Scenario
30.3.1 Classification and EOC Scenario
30.4 Technical Aspects and Techniques
30.4.1 Classification
30.4.2 Clustering
30.6 Exercises
31 Intelligent Interactive Communications
31.1 Introduction
31.2 Spoken Dialogue System
31.3 Gesture based Interaction
31.5 Visual Story Telling
31.6 Virtual Environment for Personal Assistance
31.8 Intelligent Human Machine for Communication
Application Scenario
31.9 Background Information
31.10Exercises
32 Comparisons
32.1 Generalities
32.1.1 EEG and ECG
32.1.2 Speech and biomedical applications
32.1.3 Seismic and biomedical signals
32.1.4 Speech and Images
32.2 Overall
32.3 Background Information
32.4 Exercises
Glossary
Show more
The authors offer a comprehensive guide to machine learning applied to signal processing and recognition problems, and then discuss real applications in domains such as speech processing and biomedical signal processing, with a focus on handling noise. This textbook is intended for advanced undergraduate and graduate students of computer science and engineering.
Prof. Michael M. Richter completed his PhD on mathematical logic at the University of Freiburg, and his Habilitation in mathematics at the University of Tübingen. He taught at the University of Texas at Austin and at RWTH Aachen, in addition to numerous visiting professorships. Most recently he held a chair in computer science at the University of Kaiserslautern, where he was also the founding scientific director of the DFKI (German Research Center for Artificial Intelligence). He is currently an adjunct professor at the University of Calgary. He has taught, researched, and published extensively in the areas of mathematical logic and artificial intelligence. Prof. Richter is one of the pioneers of case-based reasoning: he founded the leading European event on the subject, he led many of the key academic research projects, and he demonstrated the real-world viability of the approach with successful commercial products.
Dr. Sheuli Paul completed her PhD on a dynamic automatic noisy speech recognition system in Kaiserslautern. Her interests include speech recognition and signal processing.
Part I Realms of Signal Processing
1 Digital Signal Representation
1.1 Introduction
1.2 Numbers
1.2.1 Numbers and Numerals
1.2.2 Types of Numbers
1.3 Sampling and Reconstruction of Signals
1.3.1 Scalar Quantization
1.3.3 Signal-To-Noise Ratio
1.3.4 Transmission Rate
1.3.5 Nonuniform Quantizer
1.3.6 Companding
1.4 Data Representations
1.4.1 Fixed-Point Number Representations
1.4.2 Sign-Magnitude Format
1.4.3 One's-Complement Format
1.4.4 Two's-Complement Format
1.6 Fixed-Point Representations Based on Radix-Point
1.7 Dynamic Range
1.9 Background Information
1.10 Exercises
2 Signal Processing Background
2.1 Basic Concepts
2.2 Signals and Information
2.3 Signal Processing
ix
x Contents
2.4 Discrete Signal Representations
2.6 Parseval's Theorem
2.7 Gibbs Phenomenon
2.8 Wold Decomposition
2.9 State Space Signal Processing
2.10 Common Measurements
2.10.1 Convolution
2.10.2 Correlation
2.10.3 Auto Covariance
2.10.4 Coherence
2.10.6 Estimation and Detection
2.10.7 Central Limit Theorem
2.10.9 Machine Learning
2.10.10Exercises
3 Fundamentals of Signal Transformations
3.1 Transformation Methods
3.1.1 Laplace Transform
3.1.3 Fourier Series
3.1.4 Fourier Transform
3.1.5 Discrete Fourier Transform and Fast Fourier Transform
3.1.6 Zero Padding
3.1.7 Overlap-Add and Overlap-Save Convolution
Algorithms
3.1.8 Short Time Fourier Transform (STFT)
3.1.9 Wavelet Transform
3.1.10 Windowing Signal and the DCT Transforms
3.2 Analysis and Comparison of Transformations
3.3 Background Information
3.4 Exercises
4 Digital Filters
4.1 Introduction
4.1.2 Bilinear Transform
4.2 Windowing for Filtering
4.3 Allpass Filters
4.4 Lattice Filters
4.5 All-Zero Lattice Filter
4.6 Lattice Ladder Filters
Contents xi
4.7 Comb Filter
4.8 Notch Filter
4.10 Exercises
5 Estimation and Detection
5.2 Hypothesis Testing
5.2.1 Bayesian Hypothesis Testing
5.2.2 MAP Hypothesis Testing
5.3 Maximum Likelihood (ML) Hypothesis Testing
5.4 Standard Analysis Techniques
5.4.1 Best Linear Unbiased Estimator (BLUE)
5.4.2 Maximum Likelihood Estimator (MLE)
5.4.3 Least Squares Estimator (LSE)
5.4.4 Linear Minimum Mean Square Error Estimator
(LMMSE)
5.5 Exercises
6 Adaptive Signal Processing
6.1 Introduction
6.2 Parametric Signal Modeling
6.2.1 Parametric Estimation
6.4 Kalman Filter
6.4.1 Smoothing
6.5 Particle Filter
6.6 Fundamentals of Monte Carl
6.6.1 Importance Sampling (IS)
6.7 Non-Parametric Signal Modeling
6.8 Non-Parametric Estimation
6.8.1 Correlogram
6.8.2 Periodogram
6.10 Quadrature Mirror Filter Bank (QMF)
6.11 Background Information
7 Spectral Analysis
7.1 Introduction
7.3 Multivariate Signal Processing
7.3.1 Sub-band Coding and Subspace Analysis
7.5 Adaptive Beam Forming
xii Contents
7.6 Independent Component Analysis (ICA)
7.7 Principal Component Analysis (PCA)
7.8 Best Basis Algorithms
7.9 Background Information
7.10 Exercises
Part II Machine Learning and Recognition
8 General Learning
8.1 Introduction to Learning
8.2 The Learning Phases
8.2.1 Search and Utility
8.3.1 General Search Model
8.3.2 Preference relations
8.3.3 Different learning methods
8.3.4 Similarities
8.3.5 Learning to Recognize
8.4 Background Information
8.5 Exercises
9 Signal Processes, Learning, and Recognition
9.1 Learning
9.2 Bayesian Formalism
9.2.1 Dynamic Bayesian Theory
9.2.2 Recognition and Search
9.2.3 Influences
9.3 Subjectivity
9.5 Exercises
10 Stochastic Processes
10.2 Basic Concepts of Stochastic Processes
10.2.1 Markov Processes
10.2.3 HSM Topology
10.2.4 Learning Probabilities
10.2.5 Re-estimation
10.2.6 Redundancy
10.2.7 Data Preparation
10.2.8 Proper Redundancy Removal
10.3 Envelope Detection
10.3.1 Silence Threshold Selection
10.3.2 Pre-emphasis
10.4 Several Processes
10.4.1 Similarity
10.4.2 The Local-Global Principle
10.4.3 HSM Similarities
10.5 Conflict and Support
10.6 Examples and Applications
10.7 Predictions
10.8 Background Information
10.9 Exercises
11 Feature Extraction
11.1 Feature Extractions
11.2 Basic Techniques
11.3 Spectral Analysis and Feature Transformation
11.3.1 Parametric Feature Transformations and Cepstrum
11.3.3 Frame Energy
11.4 Linear Prediction Coe_cients (LPC)
11.5 Linear Prediction Cepstral Coe_cients (LPCC)
11.6 Adaptive Perceptual Local Trigonometric Transformation
(APLTT)
11.7 Search
11.7.1 General Search Model
11.8.1 Purpose
11.8.2 Linear Prediction
11.8.3 Mean Squared Error Minimization
11.8.4 Computation of Probability of an Observation Sequence
11.8.5 Forward and Backward Prediction
11.8.6 Forward-Backward Prediction
11.9 Background Information
11.10Exercises
12 Unsupervised Learning
12.1 Generalities
12.2 Clustering Principles
12.3 Cluster Analysis Methods
12.4.1 K-means
12.4.2 Vector Quantization (VQ)
12.4.3 Expectation Maximization (EM)
12.4.4 GMM Clustering
12.5 Background Information
12.6 Exercises
xiv Contents
13 Markov Model and Hidden Stochastic Model
13.1 Markov Process
13.2 Gaussian Mixture Model (GMM)
13.3 Advantages of using GMM
13.4 Linear Prediction Analysis
13.4.2 Yule-Walker Approach
13.4.3 Covariance Method
13.4.4 Comparison of Correlation and Covariance methods
13.5 The ULS Approach
13.6 Comparison of ULS and Covariance Methods
13.7 Forward Prediction
13.8 Backward Prediction
13.9 Forward-Backward Prediction
13.10Baum-Welch Algorithm
13.12Background Information
13.13Exercises
14 Fuzzy Logic and Rough Sets
14.1 Rough Sets
14.2 Fuzzy Sets
14.2.1 Basis Elements
14.2.2 Possibility and Necessity
14.3 Fuzzy Clustering
14.4 Fuzzy Probabilities
14.6 Exercises
15 Neural Networks
15.1.1 Neural Network Training
15.1.2 Neural Network Topology
15.2 Parallel Distributed Processing
15.2.1 Forward and Backward Uses
15.2.2 Learning
15.3 Applications to Signal Processing
15.4 Background Information
15.5 Exercises
Part III Real Aspects and Applications
16 Noisy Signals
16.1 Introduction
16.3 Sources of Noise
16.4 Noise Measurement
16.5 Weights and A-Weights
16.6 Signal to Noise Ratio (SNR)
16.7 Noise Measuring Filters and Evaluation
16.8 Types of noise
16.9 Origin of noises
16.10Box Plot Evaluation
16.11Individual noise types
16.11.2Mild
16.11.3Steady-unsteady Time varying Noise
16.11.4Strong Noise
16.12Solution to Strong Noise: Matched Filter
16.13Background Information
16.14Exercises
17 Reasoning Methods and Noise Removal
17.1 Generalities
17.2 Special Noise Removal Methods
17.2.1 Residual Noise
17.2.2 Mild Noise
17.2.3 Steady-Unsteady Noise
17.3 Poisson Distribution
17.3.1 Outliers and Shots
17.3.2 Underlying probability of Shots
17.4 Kalman Filter
17.4.1 Prediction Estimates
17.4.2 White noise Kalman filtering
17.4.3 Application of Kalman filter
17.5 Classification, Recognition and Learning
17.5.1 Summary of the used concepts
17.6 Principle Component Analysis (PCA)
17.7 Reasoning Methods
17.7.1 Case-Based Reasoning (CBR)
17.9 Exercises
xvi Contents
18 Audio Signals and Speech Recognition
18.1 Generalities of Speech
18.2 Categories of Speech Recognition
18.3 Automatic Speech Recognition
18.3.1 System Structure
18.4 Speech Production Model
18.5 Acoustics
18.6 Human Speech Production
18.6.1 The Human Speech Generation
18.6.2 Excitation
18.6.4 Unvoiced Speech
18.7 Silence Regions
18.8 Glottis
18.9 Lips
18.10Plosive Speech Source
18.11Vocal-Tract
18.13Formants
18.14Strong Noise
18.15Background Information
18.16Exercises
19 Noisy Speech
19.1 Introduction
19.2 Colored Noise
19.2.1 Additional types of Colored Noise
19.3 Poisson Processes and Shots
19.4 Matched Filters
19.5 Shot Noise
19.6 Background Information
20 Aspects Of Human Hearing
20.1 Human Ear
20.3 Critical Bands and Scales
20.3.1 Mel Scale
20.3.2 Bark Scale
20.3.3 Erb Scale
20.3.4 Greenwood Scale
20.4 Filter Banks
20.4.2 Auditory Filter Banks
20.4.3 Filter Banks
Contents xvii
20.4.4 Mel Critical Filter Bank
20.5 Psycho-acoustic Phenomena
20.5.1 Perceptual Measurement
20.5.2 Human Hearing and Perception
20.5.3 Sound Pressure Level (SPL)
20.5.4 Absolute Threshold of Hearing (ATH)
20.6 Perceptual Adaptation
20.7 Auditory System and Hearing Model
20.8 Auditory Masking and Masking Frequency
20.9 Perceptual Spectral Features
20.10Critical Band Analysis
20.11Equal Loudness Pre-emphasis
20.13Feature Transformation
20.14Filters and Human Ear
20.15Temporal Aspects
20.16Background Information
20.17Exercises
21 Speech Features
21.1 Generalities
21.2 Cost Functions
21.3 Special Feature Extractions
21.3.1 MFCC Features
21.3.2 Feature Transformation applying DCT
21.4 Background Information
22 Hidden Stochastic Model for Speech
22.1 General
22.3 Forward and Backward Predictions
22.3.1 Forward Algorithm
22.4 Forward-Backward Prediction
22.5 Burg Approach
22.6 Graph Search
22.6.1 Recognition Model with Search
22.7 Semantic Issues and Industrial Applications
22.8 Problems with Noise
22.9 Aspects of Music
22.10Music reception
22.11Background Information
xviii Contents
23 Different Speech Applications - Part A
23.1 Generalities
23.2 Example Applications
23.2.1 Experimental laboratory
23.2.2 Health care support (everyday actions)
23.2.3 Diagnostic support for persons with possible dementia
23.2.4 Noise
23.3 Background Information
23.4 Exercises
24 Different Speech Applications - Part B
24.1 Introduction
24.2 Discrete-Time Signals
24.3 Speech Processing
24.3.1 Framing
24.3.2 Pre-emphasis
24.3.4 Fourier Transform
24.3.5 Mel-Filtering
24.3.6 Mel-Frequency Cepstral Coeffcients
24.4 Speech Analysis and Sound Effects Laboratory (SASE_Lab)
24.5.1 Introduction
24.5.2 Wake-up-Word Paradigm
24.5.3 Wake-Up-Word: Definition
24.5.4 Wake-Up-Word System
24.5.5 Front-End of the Wake-Up-Word System
24.6 Conclusion
24.6.1 Wake-Up-Word: Tool Demo
24.6.2 Elevator Simulator
24.7 Background Information
24.9 Speech Analysis and Sound E_ects Laboratory (SASE_Lab)"
25 Biomedical Signals: ECG, EEG
25.1 ECG signals
25.1.1 Bioelectric Signals
25.1.2 Noise
25.2 EEG Signals
25.2.1 General properties
25.2.2 Signal types and properties
25.3 Neural Network use
25.4 Major Research Questions
25.5 Background Information
Contents xix
25.6 Exercises
26 Seismic Signals
26.1 Generalities
26.2 Sources of seismic signals
26.3 Intermediate elements
26.5 Major seismic problems
26.6 Noise
26.7 Background Information
26.8 Exercises
27 Radar Signals
27.1 Introduction
27.2 Radar Types and Applications
27.3 Doppler Equations, Ambiguity Function(AF) and Matched
Filter
27.4 Moving Target Detection
27.5 Applications and Discussions
27.6 Examples
27.8 Exercises
28 Visual Story Telling
28.1.1 Common Visualization Approaches
28.2 Analytics and Visualization
28.2.2 Visual Data Minin
28.3 Communication and Visualization
28.4 Background Information
28.5 Exercises
29 Digital Processes and Multimedia
29.1 Images
29.1.1 Digital Image Processing
29.1.2 Images as Matrices
29.1.3 Gray Scale Images
29.2 Spatial Filtering
29.2.1 Linear Filtering of Images
29.2.2 Separable Filters
29.3 Median Filtering
29.4 Color Equalization
29.4.2 Examples of Image Transformation Matrixes
xx Contents
29.5 Basic Image Statistics
29.6 Abstraction Levels of Images and its Representations
29.6.1 Lowest Level
29.6.2 Geometric Level
29.6.3 Domain Level
29.6.4 Segmentation
29.7 Background Information
30 Visualizations of Emergency Operation Centre
30.1 Introduction
30.2 Communications in Emergency Situations
30.3 Emergency Scenario
30.3.1 Classification and EOC Scenario
30.4 Technical Aspects and Techniques
30.4.1 Classification
30.4.2 Clustering
30.6 Exercises
31 Intelligent Interactive Communications
31.1 Introduction
31.2 Spoken Dialogue System
31.3 Gesture based Interaction
31.5 Visual Story Telling
31.6 Virtual Environment for Personal Assistance
31.8 Intelligent Human Machine for Communication
Application Scenario
31.9 Background Information
31.10Exercises
32 Comparisons
32.1 Generalities
32.1.1 EEG and ECG
32.1.2 Speech and biomedical applications
32.1.3 Seismic and biomedical signals
32.1.4 Speech and Images
32.2 Overall
32.3 Background Information
32.4 Exercises
Glossary
Show morePart I Realms of Signal Processing.- 1 Digital Signal
Representation.- 1.1 Introduction.- 1.2 Numbers.- 1.2.1 Numbers
and Numerals.- 1.2.2 Types of Numbers.- 1.2.3 Positional Number
Systems.- 1.3 Sampling and Reconstruction of Signals.- 1.3.1 Scalar
Quantization.- 1.3.2 Quantization Noise.- 1.3.3 Signal-To-Noise
Ratio.- 1.3.4 Transmission Rate.- 1.3.5 Nonuniform Quantizer.-
1.3.6 Companding.- 1.4 Data Representations.- 1.4.1 Fixed-Point
Number Representations.- 1.4.2 Sign-Magnitude Format.- 1.4.3
One’s-Complement Format.- 1.4.4 Two’s-Complement Format.- 1.5
Fix-Point DSP’s.- 1.6 Fixed-Point Representations Based on
Radix-Point.- 1.7 Dynamic Range.- 1.8 Precision.- 1.9 Background
Information.- 1.10 Exercises.- 2 Signal Processing
Background.- 2.1 Basic Concepts.- 2.2 Signals and Information.-
2.3 Signal Processing.- ix.- x Contents.- 2.4 Discrete Signal
Representations.- 2.5 Delta and Impulse Function.- 2.6 Parseval’s
Theorem.- 2.7 Gibbs Phenomenon.- 2.8 Wold Decomposition.- 2.9 State
Space Signal Processing.- 2.10 Common Measurements.- 2.10.1
Convolution.- 2.10.2 Correlation.- 2.10.3 Auto Covariance.- 2.10.4
Coherence.- 2.10.5 Power Spectral Density (PSD).- 2.10.6 Estimation
and Detection.- 2.10.7 Central Limit Theorem.- 2.10.8 Signal
Information Processing Types.- 2.10.9 Machine Learning.-
2.10.10Exercises.- 3 Fundamentals of Signal
Transformations.- 3.1 Transformation Methods.- 3.1.1 Laplace
Transform.- 3.1.2 Z-Transform .- 3.1.3 Fourier Series.- 3.1.4
Fourier Transform.- 3.1.5 Discrete Fourier Transform and Fast
Fourier Transform .- 3.1.6 Zero Padding.- 3.1.7 Overlap-Add and
Overlap-Save Convolution.- Algorithms.- 3.1.8 Short Time Fourier
Transform (STFT).- 3.1.9 Wavelet Transform.- 3.1.10 Windowing
Signal and the DCT Transforms.- 3.2 Analysis and Comparison of
Transformations.- 3.3 Background Information.- 3.4 Exercises.- 3.5
References.- 4 Digital Filters.- 4.1 Introduction.- 4.1.1
FIR and IIR Filters.- 4.1.2 Bilinear Transform.-4.2 Windowing for
Filtering.- 4.3 Allpass Filters.- 4.4 Lattice Filters.- 4.5
All-Zero Lattice Filter.- 4.6 Lattice Ladder Filters.- Contents
xi.- 4.7 Comb Filter.- 4.8 Notch Filter.- 4.9 Background
Information.- 4.10 Exercises.- 5 Estimation and Detection.-
5.1 Introduction.- 5.2 Hypothesis Testing.- 5.2.1 Bayesian
Hypothesis Testing.- 5.2.2 MAP Hypothesis Testing.- 5.3 Maximum
Likelihood (ML) Hypothesis Testing.- 5.4 Standard Analysis
Techniques.- 5.4.1 Best Linear Unbiased Estimator (BLUE).- 5.4.2
Maximum Likelihood Estimator (MLE).- 5.4.3 Least Squares Estimator
(LSE).- 5.4.4 Linear Minimum Mean Square Error Estimator.-
(LMMSE).- 5.5 Exercises.- 6 Adaptive Signal Processing.- 6.1
Introduction.- 6.2 Parametric Signal Modeling.- 6.2.1 Parametric
Estimation.- 6.3 Wiener Filtering.- 6.4 Kalman Filter.- 6.4.1
Smoothing.- 6.5 Particle Filter.- 6.6 Fundamentals of Monte Carl.-
6.6.1 Importance Sampling (IS).- 6.7 Non-Parametric Signal
Modeling.- 6.8 Non-Parametric Estimation.- 6.8.1 Correlogram.-
6.8.2 Periodogram.- 6.9 Filter Bank Method.- 6.10 Quadrature Mirror
Filter Bank (QMF).- 6.11 Background Information.- 6.12 Exercises.-
7 Spectral Analysis.- 7.1 Introduction.- 7.2 Adaptive
Spectral Analysis.- 7.3 Multivariate Signal Processing.- 7.3.1
Sub-band Coding and Subspace Analysis.- 7.4 Wavelet Analysis.- 7.5
Adaptive Beam Forming.- xii Contents.- 7.6 Independent Component
Analysis (ICA).- 7.7 Principal Component Analysis (PCA).- 7.8 Best
Basis Algorithms.- 7.9 Background Information.- 7.10 Exercises.-
Part II Machine Learning and Recognition.- 8 General
Learning.- 8.1 Introduction to Learning.- 8.2 The Learning
Phases.- 8.2.1 Search and Utility.- 8.3 Search.- 8.3.1 General
Search Model.- 8.3.2 Preference relations.- 8.3.3 Different
learning methods.- 8.3.4 Similarities .- 8.3.5 Learning to
Recognize.- 8.3.6 Learning again.- 8.4 Background Information.- 8.5
Exercises.- 9 Signal Processes, Learning, and Recognition.-
9.1 Learning.- 9.2Bayesian Formalism.- 9.2.1 Dynamic Bayesian
Theory.- 9.2.2 Recognition and Search.- 9.2.3 Influences.- 9.3
Subjectivity.- 9.4 Background Information.- 9.5 Exercises.- 10
Stochastic Processes.- 10.1 Preliminaries on Probabilities.-
10.2 Basic Concepts of Stochastic Processes.- 10.2.1 Markov
Processes.- 10.2.2 Hidden Stochastic Models (HSM).- 10.2.3 HSM
Topology.- 10.2.4 Learning Probabilities.- 10.2.5 Re-estimation.-
10.2.6 Redundancy.- 10.2.7 Data Preparation.- 10.2.8 Proper
Redundancy Removal.- 10.3 Envelope Detection.- 10.3.1 Silence
Threshold Selection.- 10.3.2 Pre-emphasis.- Contents xiii.- 10.4
Several Processes.- 10.4.1 Similarity.- 10.4.2 The Local-Global
Principle.- 10.4.3 HSM Similarities.- 10.5 Conflict and Support.-
10.6 Examples and Applications.- 10.7 Predictions.- 10.8 Background
Information.- 10.9 Exercises.- 11 Feature Extraction.- 11.1
Feature Extractions.- 11.2 Basic Techniques.- 11.2.1 Spectral
Shaping.- 11.3 Spectral Analysis and Feature Transformation.-
11.3.1 Parametric Feature Transformations and Cepstrum.- 11.3.2
Standard Feature Extraction Techniques.- 11.3.3 Frame Energy.- 11.4
Linear Prediction Coe_cients (LPC).- 11.5 Linear Prediction
Cepstral Coe_cients (LPCC).- 11.6 Adaptive Perceptual Local
Trigonometric Transformation.- (APLTT).- 11.7 Search.- 11.7.1
General Search Model.- 11.8 Predictions.- 11.8.1 Purpose.- 11.8.2
Linear Prediction.- 11.8.3 Mean Squared Error Minimization.- 11.8.4
Computation of Probability of an Observation Sequence.- 11.8.5
Forward and Backward Prediction.- 11.8.6 Forward-Backward
Prediction.- 11.9 Background Information.- 11.10Exercises.- 12
Unsupervised Learning.- 12.1 Generalities.- 12.2 Clustering
Principles.- 12.3 Cluster Analysis Methods.- 12.4 Special Methods.-
12.4.1 K-means.- 12.4.2 Vector Quantization (VQ).- 12.4.3
Expectation Maximization (EM).- 12.4.4 GMM Clustering.- 12.5
Background Information.- 12.6 Exercises.- xiv Contents.- 13
Markov Model and Hidden Stochastic Model.-13.1 Markov Process.-
13.2 Gaussian Mixture Model (GMM).- 13.3 Advantages of using GMM.-
13.4 Linear Prediction Analysis.- 13.4.1 Autocorrelation Method.-
13.4.2 Yule-Walker Approach.- 13.4.3 Covariance Method.- 13.4.4
Comparison of Correlation and Covariance methods.- 13.5 The ULS
Approach.- 13.6 Comparison of ULS and Covariance Methods.- 13.7
Forward Prediction.- 13.8 Backward Prediction.- 13.9
Forward-Backward Prediction.- 13.10Baum-Welch Algorithm.-
13.11Viterbi Algorithm.- 13.12Background Information.-
13.13Exercises.- 14 Fuzzy Logic and Rough Sets.- 14.1 Rough
Sets.- 14.2 Fuzzy Sets.- 14.2.1 Basis Elements.- 14.2.2 Possibility
and Necessity.- 14.3 Fuzzy Clustering.- 14.4 Fuzzy Probabilities.-
14.5 Background Information.- 14.6 Exercises.- 15 Neural
Networks.- 15.1 Neural Network Types.- 15.1.1 Neural Network
Training.- 15.1.2 Neural Network Topology.- 15.2 Parallel
Distributed Processing.- 15.2.1 Forward and Backward Uses.- 15.2.2
Learning.- 15.3 Applications to Signal Processing.- 15.4 Background
Information.- 15.5 Exercises.- Part III Real Aspects and
Applications.- Contents xv.- 16 Noisy Signals.- 16.1
Introduction.- 16.2 Noise Questions.- 16.3 Sources of Noise.- 16.4
Noise Measurement.- 16.5 Weights and A-Weights.- 16.6 Signal to
Noise Ratio (SNR).- 16.7 Noise Measuring Filters and Evaluation.-
16.8 Types of noise.- 16.9 Origin of noises.- 16.10Box Plot
Evaluation.- 16.11Individual noise types.- 16.11.1Residual.-
16.11.2Mild.- 16.11.3Steady-unsteady Time varying Noise.-
16.11.4Strong Noise.- 16.12Solution to Strong Noise: Matched
Filter.- 16.13Background Information.- 16.14Exercises.- 17
Reasoning Methods and Noise Removal.- 17.1 Generalities.- 17.2
Special Noise Removal Methods.- 17.2.1 Residual Noise.- 17.2.2 Mild
Noise.- 17.2.3 Steady-Unsteady Noise.- 17.2.4 Strong Noise.- 17.3
Poisson Distribution.- 17.3.1 Outliers and Shots.- 17.3.2
Underlying probability of Shots.- 17.4 Kalman Filter.- 17.4.1
Prediction Estimates.-17.4.2 White noise Kalman filtering.- 17.4.3
Application of Kalman filter.- 17.5 Classification, Recognition and
Learning.- 17.5.1 Summary of the used concepts.- 17.6 Principle
Component Analysis (PCA).- 17.7 Reasoning Methods.- 17.7.1
Case-Based Reasoning (CBR).- 17.8 Background Information.- 17.9
Exercises.- xvi Contents.- 18 Audio Signals and Speech
Recognition.- 18.1 Generalities of Speech.- 18.2 Categories of
Speech Recognition.- 18.3 Automatic Speech Recognition.- 18.3.1
System Structure.- 18.4 Speech Production Model.- 18.5 Acoustics.-
18.6 Human Speech Production.- 18.6.1 The Human Speech Generation.-
18.6.2 Excitation.- 18.6.3 Voiced Speech.- 18.6.4 Unvoiced Speech.-
18.7 Silence Regions.- 18.8 Glottis.- 18.9 Lips.- 18.10Plosive
Speech Source.- 18.11Vocal-Tract.- 18.12Parametric and
Non-Parametric Models.- 18.13Formants.- 18.14Strong Noise.-
18.15Background Information.- 18.16Exercises.- 19 Noisy
Speech.- 19.1 Introduction.- 19.2 Colored Noise.- 19.2.1
Additionaltypes of Colored Noise.- 19.3 Poisson Processes and
Shots.- 19.4 Matched Filters.- 19.5 Shot Noise.- 19.6 Background
Information.- 19.7 Exercises.- 20 Aspects Of Human Hearing.-
20.1 Human Ear.- 20.2 Human Auditory System.- 20.3 Critical Bands
and Scales.- 20.3.1 Mel Scale.- 20.3.2 Bark Scale.- 20.3.3 Erb
Scale.- 20.3.4 Greenwood Scale.- 20.4 Filter Banks.- 20.4.1 ICA
Network.- 20.4.2 Auditory Filter Banks.- 20.4.3 Filter Banks.-
Contents xvii.- 20.4.4 Mel Critical Filter Bank.- 20.5
Psycho-acoustic Phenomena.- 20.5.1 Perceptual Measurement.- 20.5.2
Human Hearing and Perception.- 20.5.3 Sound Pressure Level (SPL).-
20.5.4 Absolute Threshold of Hearing (ATH).- 20.6 Perceptual
Adaptation.- 20.7 Auditory System and Hearing Model.- 20.8 Auditory
Masking and Masking Frequency.- 20.9 Perceptual Spectral Features.-
20.10Critical Band Analysis.- 20.11Equal Loudness Pre-emphasis.-
20.12Perceptual Transformation.- 20.13Feature Transformation.-
20.14Filters and Human Ear.- 20.15Temporal Aspects.-
20.16Background Information.- 20.17Exercises.- 21 Speech
Features.- 21.1 Generalities.- 21.2 Cost Functions.- 21.3
Special Feature Extractions.- 21.3.1 MFCC Features.- 21.3.2 Feature
Transformation applying DCT.- 21.4 Background Information.- 21.5
Exercises.- 22 Hidden Stochastic Model for Speech.- 22.1
General.- 22.2 Hidden Stochastic Model.- 22.3 Forward and Backward
Predictions.- 22.3.1 Forward Algorithm.- 22.3.2 Backward
Algorithm.- 22.4 Forward-Backward Prediction.- 22.5 Burg Approach.-
22.6 Graph Search.- 22.6.1 Recognition Model with Search.- 22.7
Semantic Issues and Industrial Applications.- 22.8 Problems with
Noise.- 22.9 Aspects of Music.- 22.10Music reception.-
22.11Background Information.- 22.12Exercises.- xviii Contents.-
23 Different Speech Applications – Part A.- 23.1
Generalities.- 23.2 Example Applications.- 23.2.1 Experimental
laboratory.- 23.2.2 Health care support (everyday actions).- 23.2.3
Diagnostic support for persons with possible dementia.- 23.2.4
Noise.- 23.3 Background Information.- 23.4 Exercises.- 24
Different Speech Applications – Part B.- 24.1
Introduction.- 24.2 Discrete-Time Signals.- 24.3 Speech
Processing.- 24.3.1 Framing.- 24.3.2 Pre-emphasis.- 24.3.3
Windowing.- 24.3.4 Fourier Transform.- 24.3.5 Mel-Filtering.-
24.3.6 Mel-Frequency Cepstral Coeffcients.- 24.4 Speech Analysis
and Sound Effects Laboratory (SASE_Lab).- 24.5 Wake-Up-Word Speech
Recognition.- 24.5.1 Introduction.- 24.5.2 Wake-up-Word Paradigm.-
24.5.3 Wake-Up-Word: Definition.- 24.5.4 Wake-Up-Word System.-
24.5.5 Front-End of the Wake-Up-Word System.- 24.6 Conclusion.-
24.6.1 Wake-Up-Word: Tool Demo.- 24.6.2 Elevator Simulator.- 24.7
Background Information.- 24.8 Exercises.- 24.9 Speech Analysis and
Sound E_ects Laboratory (SASE_Lab)" .- 25 Biomedical Signals:
ECG, EEG.- 25.1 ECG signals.- 25.1.1 Bioelectric Signals.-
25.1.2 Noise.- 25.2 EEG Signals.- 25.2.1 General properties.-
25.2.2 Signal types and properties.- 25.2.3 Disadvantages.- 25.3
Neural Network use.- 25.4 Major Research Questions.- 25.5
Background Information.- Contents xix.- 25.6 Exercises.- 26
Seismic Signals.- 26.1 Generalities.- 26.2 Sources of seismic
signals.- 26.3 Intermediate elements.- 26.4 Practical Data
Sources.- 26.5 Major seismic problems.- 26.6 Noise.- 26.7
Background Information.- 26.8 Exercises.- 27 Radar Signals.-
27.1 Introduction.- 27.2 Radar Types and Applications.- 27.3
Doppler Equations, Ambiguity Function(AF) and Matched.- Filter.-
27.4 Moving Target Detection.- 27.5 Applications and Discussions.-
27.6 Examples.- 27.7 Background Information.- 27.8 Exercises.-
28 Visual Story Telling.- 28.1 Introduction.- 28.1.1 Common
Visualization Approaches.- 28.2 Analytics and Visualization.-
28.2.1 Visualization.- 28.2.2 Visual Data Minin.- 28.3
Communication and Visualization.- 28.4 Background Information.-
28.5 Exercises.- 29 Digital Processes and Multimedia.- 29.1
Images.- 29.1.1 DigitalImage Processing.- 29.1.2 Images as
Matrices.- 29.1.3 Gray Scale Images.- 29.2 Spatial Filtering.-
29.2.1 Linear Filtering of Images.- 29.2.2 Separable Filters.-
29.2.3 Mechanics of Linear Spatial Filtering Operation.- 29.3
Median Filtering.- 29.4 Color Equalization.- 29.4.1 Image
Transformations.- 29.4.2 Examples of Image Transformation
Matrixes.- xx Contents.- 29.5 Basic Image Statistics.- 29.6
Abstraction Levels of Images and its Representations.- 29.6.1
Lowest Level.- 29.6.2 Geometric Level.- 29.6.3 Domain Level.-
29.6.4 Segmentation.- 29.7 Background Information.- 29.8
Exercises.- 30 Visualizations of Emergency Operation
Centre.- 30.1 Introduction.- 30.2 Communications in Emergency
Situations.- 30.3 Emergency Scenario.- 30.3.1 Classification and
EOC Scenario.- 30.4 Technical Aspects and Techniques.- 30.4.1
Classification.- 30.4.2 Clustering.- 30.5 Background Information.-
30.6 Exercises.- 31 Intelligent Interactive Communications.-
31.1 Introduction.- 31.2 Spoken Dialogue System.- 31.3 Gesture
based Interaction.- 31.4 Object Recognition and Identification.-
31.5 Visual Story Telling.- 31.6 Virtual Environment for Personal
Assistance.- 31.7 Sensor Fusion.- 31.8 Intelligent Human Machine
for Communication.- Application Scenario.- 31.9 Background
Information.- 31.10Exercises.- 32 Comparisons.- 32.1
Generalities.- 32.1.1 EEG and ECG.- 32.1.2 Speech and biomedical
applications.- 32.1.3 Seismic and biomedical signals.- 32.1.4
Speech and Images.- 32.2 Overall.- 32.3 Background Information.-
32.3.1 General.- 32.4 Exercises.- Glossary.
Professor Michael M. Richter taught at the University of Texas at Austin and at RWTH Aachen, in addition to numerous visiting professorships. He is one of the founding scientific director of the DFKI (German Research Center for Artificial Intelligence). He taught, researched, and published extensively in the areas of mathematical logic and artificial intelligence. Professor Richter was one of the pioneers of case-based reasoning: he founded the leading European event on the subject, he led many of the key academic research projects, and demonstrated the real-world viability of the approach with successful commercial products. Michael Richter passed away during the final publishing phase of this book.
Dr. Sheuli Paul is a scientist in Defence Research and Development Canada, engaged in applied research in the areas of signal processing, machine learning, artificial intelligence and human-robot interaction. Trying to solve complex problems in interdisciplinary areas is her passion.
Dr. Veton Këpuska is an inventor of Wake-Up-Word Speech Recognition, a method of communication with machines for which he was granted two patents. He joined Florida Institute of Technology (FIT) in 2003 and engaged in numerous research activities in speech and image processing, digital processes, and machine learning. Dr. Këpuska won the First Annual Digital Signal Processing Design competition by applying his Wake-up-Word on embedded Analog Devices Platform. Dr. Këpuska won numerous awards including “the Kerry Bruce Clark” award for teaching excellence and received numerous best paper awards.
Prof. Marius Silaghi has taught, researched, and published in the areas of artificial intelligence and networking. Professor Silaghi is involved in human-machine interaction research and proposed techniques for motion capture, speech recognition, and robotics. He founded the conference on Distributed Constraint Optimization and gave multiple tutorials on the topic at the main artificial intelligence conferences. He received numerous best paper awards.
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