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  • Produktbild: Signal Processing and Machine Learning with Applications
  • Produktbild: Signal Processing and Machine Learning with Applications

Signal Processing and Machine Learning with Applications

Fr. 81.90

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.10.2022

Abbildungen

XLI, 607 p. 300 illus., 237 illus. in color.

Verlag

Springer

Seitenzahl

607

Maße (L/B/H)

24.1/16/4.1 cm

Gewicht

1081 g

Auflage

1st ed. 2022

Sprache

Englisch

ISBN

978-3-319-45371-2

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.10.2022

Abbildungen

XLI, 607 p. 300 illus., 237 illus. in color.

Verlag

Springer

Seitenzahl

607

Maße (L/B/H)

24.1/16/4.1 cm

Gewicht

1081 g

Auflage

1st ed. 2022

Sprache

Englisch

ISBN

978-3-319-45371-2

Herstelleradresse

Springer-Verlag GmbH
Europaplatz 3
69115 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Signal Processing and Machine Learning with Applications
  • Produktbild: Signal Processing and Machine Learning with Applications

  • 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 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 Di
    ff
    erent 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 Di
    ff
    erent 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
    .