Signal processing and machine learni...
Richter, Michael M.

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  • Signal processing and machine learning with applications
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Signal processing and machine learning with applications/ by Michael M. Richter ... [et al.].
    其他作者: Richter, Michael M.
    出版者: Cham :Springer International Publishing : : 2022.,
    面頁冊數: xli, 607 p. :ill., digital ;24 cm.
    內容註: 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 -- 9.2 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
    內容註: 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 Masking Frequency -- 20.
    Contained By: Springer Nature eBook
    標題: Signal processing - Digital techniques. -
    電子資源: https://doi.org/10.1007/978-3-319-45372-9
    ISBN: 9783319453729
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