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Signal Processing and Machine Learning with Applications
ISBN-13: 9783319453712 / Angielski / Twarda / 2022 / 355 str.
Signal Processing and Machine Learning with Applications
ISBN-13: 9783319453712 / Angielski / Twarda / 2022 / 355 str.
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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.
Wydanie ilustrowane
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
1.2.3 Positional Number Systems1.3 Sampling and Reconstruction of Signals
1.3.1 Scalar Quantization
1.3.2 Quantization Noise1.3.3 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’s1.6 Fixed-Point Representations Based on Radix-Point
1.7 Dynamic Range
1.8 Precision1.9 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 Function2.6 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 Types2.10.9 Machine Learning
2.10.10Exercises
3 Fundamentals of Signal Transformations
3.1 Transformation Methods
3.1.1 Laplace Transform
3.1.2 Z-Transform3.1.3 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 References4 Digital Filters
4.1 Introduction
4.1.1 FIR and IIR Filters4.1.2 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 Information4.10 Exercises
5 Estimation and Detection
5.1 Introduction5.2 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 Filtering6.4 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 Method6.10 Quadrature Mirror Filter Bank (QMF)
6.11 Background Information
6.12 Exercises7 Spectral Analysis
7.1 Introduction
7.2 Adaptive Spectral Analysis7.3 Multivariate Signal Processing
7.3.1 Sub-band Coding and Subspace Analysis
7.4 Wavelet Analysis7.5 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 Search8.3.1 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 again8.4 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
9.4 Background Information9.5 Exercises
10 Stochastic Processes
10.1 Preliminaries on Probabilities10.2 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 xiii10.4 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 Shaping11.3 Spectral Analysis and Feature Transformation
11.3.1 Parametric Feature Transformations and Cepstrum
11.3.2 Standard Feature Extraction Techniques11.3.3 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 Predictions11.8.1 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 Methods12.4.1 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 Method13.4.2 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 Algorithm13.12Background 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 Information14.6 Exercises
15 Neural Networks
15.1 Neural Network Types15.1.1 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 xv16 Noisy Signals
16.1 Introduction
16.2 Noise Questions16.3 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
16.11.1Residual16.11.2Mild
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 Noise17.3 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 Information17.9 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 Speech18.6.4 Unvoiced Speech
18.7 Silence Regions
18.8 Glottis
18.9 Lips
18.10Plosive Speech Source
18.11Vocal-Tract
18.12Parametric and Non-Parametric Models18.13Formants
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
19.7 Exercises20 Aspects Of Human Hearing
20.1 Human Ear
20.2 Human Auditory System20.3 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 Network20.4.2 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 Transformation20.13Feature 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 Exercises22 Hidden Stochastic Model for Speech
22.1 General
22.2 Hidden Stochastic Model22.3 Forward and Backward Predictions
22.3.1 Forward Algorithm
22.3.2 Backward Algorithm22.4 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
22.12Exercisesxviii 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 Windowing24.3.4 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 Recognition24.5.1 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 Exercises24.9 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 Disadvantages25.3 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 Sources26.5 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 Information27.8 Exercises
28 Visual Story Telling
28.1 Introduction28.1.1 Common Visualization Approaches
28.2 Analytics and Visualization
28.2.1 Visualization28.2.2 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
29.2.3 Mechanics of Linear Spatial Filtering Operation29.3 Median Filtering
29.4 Color Equalization
29.4.1 Image Transformations29.4.2 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 Exercises30 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 Information30.6 Exercises
31 Intelligent Interactive Communications
31.1 Introduction
31.2 Spoken Dialogue System
31.3 Gesture based Interaction
31.4 Object Recognition and Identification31.5 Visual Story Telling
31.6 Virtual Environment for Personal Assistance
31.7 Sensor Fusion31.8 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 General32.4 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.
Signal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents different kinds of signals humans use and applies them for human machine interaction to communicate.
Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications. It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence. The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes. The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engaged with signal processing, machine learning and the applications.
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