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Table of Contents
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 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 Ones-Complement Format
1.4.4 Twos-Complement Format
1.5 Fix-Point DSPs
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 Parsevals 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
9.2 Bayesian Formalism
9.2.1 Dynamic Bayesian Theory
9.2.2 Recognition and Search
9.2.3 Influences
9.3 Subjectivity
9.4 Background 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 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 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.
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 Ones-Complement Format
1.4.4 Twos-Complement Format
1.5 Fix-Point DSPs
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 Parsevals 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
9.2 Bayesian Formalism
9.2.1 Dynamic Bayesian Theory
9.2.2 Recognition and Search
9.2.3 Influences
9.3 Subjectivity
9.4 Background 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 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 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.