Digital signal processing (DSP) Books

Book SynopsisThis book describes several modules of the Code Excited Linear Prediction (CELP) algorithm. The authors use the Federal Standard-1016 CELP MATLAB® software to describe in detail several functions and parameter computations associated with analysis-by-synthesis linear prediction. The book begins with a description of the basics of linear prediction followed by an overview of the FS-1016 CELP algorithm. Subsequent chapters describe the various modules of the CELP algorithm in detail. In each chapter, an overall functional description of CELP modules is provided along with detailed illustrations of their MATLAB® implementation. Several code examples and plots are provided to highlight some of the key CELP concepts. Link to MATLAB® code found within the book Table of Contents: Introduction to Linear Predictive Coding / Autocorrelation Analysis and Linear Prediction / Line Spectral Frequency Computation / Spectral Distortion / The Codebook Search / The FS-1016 DecoderTable of ContentsIntroduction to Linear Predictive Coding.- Autocorrelation Analysis and Linear Prediction.- Line Spectral Frequency Computation.- Spectral Distortion.- The Codebook Search.- The FS-1016 Decoder.

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Book SynopsisFrom the early pulse code modulation-based coders to some of the recent multi-rate wideband speech coding standards, the area of speech coding made several significant strides with an objective to attain high quality of speech at the lowest possible bit rate. This book presents some of the recent advances in linear prediction (LP)-based speech analysis that employ perceptual models for narrow- and wide-band speech coding. The LP analysis-synthesis framework has been successful for speech coding because it fits well the source-system paradigm for speech synthesis. Limitations associated with the conventional LP have been studied extensively, and several extensions to LP-based analysis-synthesis have been proposed, e.g., the discrete all-pole modeling, the perceptual LP, the warped LP, the LP with modified filter structures, the IIR-based pure LP, all-pole modeling using the weighted-sum of LSP polynomials, the LP for low frequency emphasis, and the cascade-form LP. These extensions can be classified as algorithms that either attempt to improve the LP spectral envelope fitting performance or embed perceptual models in the LP. The first half of the book reviews some of the recent developments in predictive modeling of speech with the help of Matlab™ Simulation examples. Advantages of integrating perceptual models in low bit rate speech coding depend on the accuracy of these models to mimic the human performance and, more importantly, on the achievable "coding gains" and "computational overhead" associated with these physiological models. Methods that exploit the masking properties of the human ear in speech coding standards, even today, are largely based on concepts introduced by Schroeder and Atal in 1979. For example, a simple approach employed in speech coding standards is to use a perceptual weighting filter to shape the quantization noise according to the masking properties of the human ear. The second half of the book reviews some of the recent developments in perceptual modeling of speech (e.g., masking threshold, psychoacoustic models, auditory excitation pattern, and loudness) with the help of Matlab™ simulations. Supplementary material including Matlab™ programs and simulation examples presented in this book can also be accessed here. Table of Contents: Introduction / Predictive Modeling of Speech / Perceptual Modeling of SpeechTable of ContentsIntroduction.- Predictive Modeling of Speech.- Perceptual Modeling of Speech.

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Book SynopsisThe MPEG-1 Layer III (MP3) algorithm is one of the most successful audio formats for consumer audio storage and for transfer and playback of music on digital audio players. The MP3 compression standard along with the AAC (Advanced Audio Coding) algorithm are associated with the most successful music players of the last decade. This book describes the fundamentals and the MATLAB implementation details of the MP3 algorithm. Several of the tedious processes in MP3 are supported by demonstrations using MATLAB software. The book presents the theoretical concepts and algorithms used in the MP3 standard. The implementation details and simulations with MATLAB complement the theoretical principles. The extensive list of references enables the reader to perform a more detailed study on specific aspects of the algorithm and gain exposure to advancements in perceptual coding. Table of Contents: Introduction / Analysis Subband Filter Bank / Psychoacoustic Model II / MDCT / Bit Allocation, Quantization and Coding / DecoderTable of ContentsIntroduction.- Analysis Subband Filter Bank.- Psychoacoustic Model II.- MDCT.- Bit Allocation, Quantization and Coding.- Decoder.

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Book SynopsisIn this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-function form. This common form enables the use of the growth transformation (or extended Baum–Welch) optimization framework in discriminative learning of model parameters. In addition to all the necessary introduction of the background and tutorial material on the subject, we also included technical details on the derivation of the parameter optimization formulas for exponential-family distributions, discrete hidden Markov models (HMMs), and continuous-density HMMs in discriminative learning. Selected experimental results obtained by the authors in firsthand are presented to show that discriminative learning can lead to superior speech recognition performance over conventional parameter learning. Details on major algorithmic implementation issues with practical significance are provided to enable the practitioners to directly reproduce the theory in the earlier part of the book into engineering practice. Table of Contents: Introduction and Background / Statistical Speech Recognition: A Tutorial / Discriminative Learning: A Unified Objective Function / Discriminative Learning Algorithm for Exponential-Family Distributions / Discriminative Learning Algorithm for Hidden Markov Model / Practical Implementation of Discriminative Learning / Selected Experimental Results / Epilogue / Major Symbols Used in the Book and Their Descriptions / Mathematical Notation / BibliographyTable of ContentsIntroduction and Background.- Statistical Speech Recognition: A Tutorial.- Discriminative Learning: A Unified Objective Function.- Discriminative Learning Algorithm for Exponential-Family Distributions.- Discriminative Learning Algorithm for Hidden Markov Model.- Practical Implementation of Discriminative Learning.- Selected Experimental Results.- Epilogue.- Major Symbols Used in the Book and Their Descriptions.- Mathematical Notation.- Bibliography.

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Book SynopsisPeriodic signals can be decomposed into sets of sinusoids having frequencies that are integer multiples of a fundamental frequency. The problem of finding such fundamental frequencies from noisy observations is important in many speech and audio applications, where it is commonly referred to as pitch estimation. These applications include analysis, compression, separation, enhancement, automatic transcription and many more. In this book, an introduction to pitch estimation is given and a number of statistical methods for pitch estimation are presented. The basic signal models and associated estimation theoretical bounds are introduced, and the properties of speech and audio signals are discussed and illustrated. The presented methods include both single- and multi-pitch estimators based on statistical approaches, like maximum likelihood and maximum a posteriori methods, filtering methods based on both static and optimal adaptive designs, and subspace methods based on the principles of subspace orthogonality and shift-invariance. The application of these methods to analysis of speech and audio signals is demonstrated using both real and synthetic signals, and their performance is assessed under various conditions and their properties discussed. Finally, the estimators are compared in terms of computational and statistical efficiency, generalizability and robustness. Table of Contents: Fundamentals / Statistical Methods / Filtering Methods / Subspace Methods / Amplitude EstimationTable of ContentsFundamentals.- Statistical Methods.- Filtering Methods.- Subspace Methods.- Amplitude Estimation.

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Book SynopsisThis book focuses on a class of single-channel noise reduction methods that are performed in the frequency domain via the short-time Fourier transform (STFT). The simplicity and relative effectiveness of this class of approaches make them the dominant choice in practical systems. Even though many popular algorithms have been proposed through more than four decades of continuous research, there are a number of critical areas where our understanding and capabilities still remain quite rudimentary, especially with respect to the relationship between noise reduction and speech distortion. All existing frequency-domain algorithms, no matter how they are developed, have one feature in common: the solution is eventually expressed as a gain function applied to the STFT of the noisy signal only in the current frame. As a result, the narrowband signal-to-noise ratio (SNR) cannot be improved, and any gains achieved in noise reduction on the fullband basis come with a price to pay, which is speech distortion. In this book, we present a new perspective on the problem by exploiting the difference between speech and typical noise in circularity and interframe self-correlation, which were ignored in the past. By gathering the STFT of the microphone signal of the current frame, its complex conjugate, and the STFTs in the previous frames, we construct several new, multiple-observation signal models similar to a microphone array system: there are multiple noisy speech observations, and their speech components are correlated but not completely coherent while their noise components are presumably uncorrelated. Therefore, the multichannel Wiener filter and the minimum variance distortionless response (MVDR) filter that were usually associated with microphone arrays will be developed for single-channel noise reduction in this book. This might instigate a paradigm shift geared toward speech distortionless noise reduction techniques. Table of Contents: Introduction / Problem Formulation / Performance Measures / Linear and Widely Linear Models / Optimal Filters with Model 1 / Optimal Filters with Model 2 / Optimal Filters with Model 3 / Optimal Filters with Model 4 / Experimental StudyTable of ContentsIntroduction.- Problem Formulation.- Performance Measures.- Linear and Widely Linear Models.- Optimal Filters with Model 1.- Optimal Filters with Model 2.- Optimal Filters with Model 3.- Optimal Filters with Model 4.- Experimental Study.

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Book SynopsisThis book introduces the theory, algorithms, and implementation techniques for efficient decoding in speech recognition mainly focusing on the Weighted Finite-State Transducer (WFST) approach. The decoding process for speech recognition is viewed as a search problem whose goal is to find a sequence of words that best matches an input speech signal. Since this process becomes computationally more expensive as the system vocabulary size increases, research has long been devoted to reducing the computational cost. Recently, the WFST approach has become an important state-of-the-art speech recognition technology, because it offers improved decoding speed with fewer recognition errors compared with conventional methods. However, it is not easy to understand all the algorithms used in this framework, and they are still in a black box for many people. In this book, we review the WFST approach and aim to provide comprehensive interpretations of WFST operations and decoding algorithms to help anyone who wants to understand, develop, and study WFST-based speech recognizers. We also mention recent advances in this framework and its applications to spoken language processing. Table of Contents: Introduction / Brief Overview of Speech Recognition / Introduction to Weighted Finite-State Transducers / Speech Recognition by Weighted Finite-State Transducers / Dynamic Decoders with On-the-fly WFST Operations / Summary and PerspectiveTable of ContentsIntroduction.- Brief Overview of Speech Recognition.- Introduction to Weighted Finite-State Transducers.- Speech Recognition by Weighted Finite-State Transducers.- Dynamic Decoders with On-the-fly WFST Operations.- Summary and Perspective.

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Book SynopsisThis book addresses the problem of articulatory speech synthesis based on computed vocal tract geometries and the basic physics of sound production in it. Unlike conventional methods based on analysis/synthesis using the well-known source filter model, which assumes the independence of the excitation and filter, we treat the entire vocal apparatus as one mechanical system that produces sound by means of fluid dynamics. The vocal apparatus is represented as a three-dimensional time-varying mechanism and the sound propagation inside it is due to the non-planar propagation of acoustic waves through a viscous, compressible fluid described by the Navier-Stokes equations. We propose a combined minimum energy and minimum jerk criterion to compute the dynamics of the vocal tract during articulation. Theoretical error bounds and experimental results show that this method obtains a close match to the phonetic target positions while avoiding abrupt changes in the articulatory trajectory. The vocal folds are set into aerodynamic oscillation by the flow of air from the lungs. The modulated air stream then excites the moving vocal tract. This method shows strong evidence for source-filter interaction. Based on our results, we propose that the articulatory speech production model has the potential to synthesize speech and provide a compact parameterization of the speech signal that can be useful in a wide variety of speech signal processing problems. Table of Contents: Introduction / Literature Review / Estimation of Dynamic Articulatory Parameters / Construction of Articulatory Model Based on MRI Data / Vocal Fold Excitation Models / Experimental Results of Articulatory Synthesis / ConclusionTable of ContentsIntroduction.- Literature Review.- Estimation of Dynamic Articulatory Parameters.- Construction of Articulatory Model Based on MRI Data.- Vocal Fold Excitation Models.- Experimental Results of Articulatory Synthesis.- Conclusion.

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Book SynopsisThis book provides a comprehensive guide to 3D Light-Field camera based imaging, exploring the working principles, developments and its applications in fluid mechanics and aerodynamics measurements. It begins by discussing the fundamentals of Light-Field imaging and theoretical resolution analysis, before touching upon the detailed optics design and micro-lens array assembly. Subsequently, Light-Field calibration methods that compensate for optical distortions and establish the relations between the image and real-word 3D coordinates are covered. This is followed by Light-Field 3D reconstruction algorithms which are elaborated for micrometer-scale particles and centimeter-scale physical models. Last but not least, implementations of the preceding procedures to selected fundamental and applied flow measurement scenarios are provided at the end of the book. Development and Application of Light-Field Cameras in Fluid Measurements gives an in-depth analysis of each topic discussed, making it ideal as both an introductory and reference guide for researchers and postgraduates interested in 3D flow measurements.Table of ContentsIntroduction.- Light-field camera working principles.- Volumetric calibration for light-field camera with regular and scheimpflug lens.- Light-field particle image velocimetry.- Simultaneous 3D surface geometry and pressure distribution measurement.- Light-field PIV implementation and case studies.- Future developments of Light-field based measurements.

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Book SynopsisThis book covers the theory of multidimensional signals and systems and related practical aspects. It extends the properties and mathematical tools of one-dimensional signals and systems to multiple dimensions and covers relevant timeless topics including multidimensional transformations, multidimensional sampling as well as discrete multidimensional systems. A special emphasis is placed on physical systems described by partial differential equations, the construction of suitable integral transformations and the implementation of the corresponding discrete-time algorithms. To this end, signal spaces and functional transformations are introduced at a mathematical level provided by undergraduate programs in engineering and science.The presentation takes a comprehensive, illustrative and educational approach without reference to a particular application field. Instead, the book builds a solid theoretical concept of multidimensional signals and systems and shows the application to various problems relevant for practical scenarios.Table of Contents

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Book SynopsisThis book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.Table of ContentsRobust Speaker Verification – A Review.- Speaker Verification in Noisy Environments using Gaussian Mixture Models.- Stochastic Feature Compensation for Robust Speaker Verification.- Robust Speaker Modeling for Speaker Verification in Noisy Environments.

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Book SynopsisSignal 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. Table of ContentsPart I Realms of Signal Processing1 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 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 20.9 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 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 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

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Book SynopsisIn this new edition of the Handbook of Signal Processing Systems, many of the chapters from the previous editions have been updated, and several new chapters have been added. The new contributions include chapters on signal processing methods for light field displays, throughput analysis of dataflow graphs, modeling for reconfigurable signal processing systems, fast Fourier transform architectures, deep neural networks, programmable architectures for histogram of oriented gradients processing, high dynamic range video coding, system-on-chip architectures for data analytics, analysis of finite word-length effects in fixed-point systems, and models of architecture. There are more than 700 tables and illustrations; in this edition over 300 are in color. This new edition of the handbook is organized in three parts. Part I motivates representative applications that drive and apply state-of-the art methods for design and implementation of signal processing systems; Part II discusses architectures for implementing these applications; and Part III focuses on compilers, as well as models of computation and their associated design tools and methodologies. Table of Contents1 Signal Processing Methods for Light Field Displays. 2 Inertial Sensors and Their Applications.- 3 Finding It Now: Construction and Configuration of Networked Classifiers in Real-Time Stream Mining Systems.- 4 Deep Neural Networks: A Signal Processing Perspective.- 5 High Dynamic Range Video Cooling.- 6 Signal Processing for Control.- 7 MPEG Reconfigurable Video Coding.- 8 Signal Processing for Wireless Transceivers.- 9 Signal Processing for Radio Astronomy.- 10 Distributed Smart Cameras and Distributed Computer Vision.- 11 Arithmetic.- 12 Coarse Grained Reconfigurable Array Architectures.- 13 High Performance Stream Processing on FPGA.- 14 Application-specific Accelerators for Communications.- 15 System-on-chip Architectures for Data Analytics.- 16 Architectures for Stereo Vision.- 17 Hardware Architectures for the Fast Fourier Transform.- 18 Programmable Architectures for Histogram of Oriented Gradients Processing.- 19 Methods and Tools for Mapping Process Networks onto Multi-Processor Systems-on-chip.- 20 Intermediate Representations for Simulation and Implementation.- 21 Throughput Analysis of Dataflow Graphs.- 22 Dataflow Modeling for Reconfigurable Signal Processing Systems.- 23 Integrated Modeling Using Finite State Machines and Dataflow Graphs.- 24 Kahn Process Networks and a Reactive Extension.- 25 Decidable Signal Processing Dataflow Graphs.- 26 Systolic Arrays.- 27 Compiling for VLIW DSPs.- 28 Software Compilation Techniques for Heterogeneous Embedded Multi-Core Systems.- 29 Analysis of Finite Word-Length Effects in Fixed-Point Systems.- 30 Models of Architecture for DSP Systems.- 31 Optimization of Number Representations.- 32 Dynamic Dataflow Graphs.

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Book SynopsisDieses Übungsbuch illustriert Methoden der Nachrichtentechnik anhand ausgewählter Problemstellungen. Ein PC wird zur Bearbeitung nicht benötigt. Insgesamt 74 Aufgaben inklusive ausführlicher Lösungen führen den Leser an Themen der Systemtheorie, analoger und digitaler Modulationsverfahren sowie der Mobilfunkübertragung heran. Die Struktur orientiert sich an der sechsten Auflage des Lehrbuchs Nachrichtenübertragung.Table of ContentsSignale und Systeme.- Rauschsignale – Eigenschaften von Übertragungskanälen.- Analoge Modulationsverfahren.- Lineare Verzerrungen und Rauschen.- Systembeispiele.- Grundlagen der Datenübertragung.- Digitale Modulation.- Demodulationsstrukturen.- AGN-Übertragung.- Entzerrung.- Viterbi-Algorithmus.- Kanalschätzung.- Funkkanalübertragung.- OFDM.- CDMA.- Mehrantennensysteme.

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Book SynopsisThis book provides a reference guide for researchers, scientists and industrialists working in the area of soft computing, and highlights the latest advances in and applications of soft computing techniques in multidisciplinary areas. Gathering papers presented at the International Conference on Soft Computing: Theories and Applications (SoCTA 2016), which was held in Jaipur, Rajasthan, India, on December 28–30, 2016, it focuses on applying soft computing to solve real-life problems arising in various domains, from medical and healthcare to supply chain management, image processing and cryptanalysis. The term soft computing represents an umbrella term for computational techniques like fuzzy logic, neural networks and nature inspired algorithms. In the past few decades, there has been an exponential rise in the application of soft computing techniques to address complex and intricate problems in diverse spheres of life. The versatility of these techniques has made them a favourite among scientists and researchers alike.Table of ContentsA Brain like Computer Made of Time crystal: Could a Metric of Prime alone Replace a User and Alleviate Programming Forever?.- Optimum Selection of Energy-Efficient Material: A MCDM Based Distance Approach.- Role of Sodium, Potassium and Synaptic Conductance in STN-GPe Model of Basal Ganglia in Parkinson Disease.- A New Hybrid Algorithm Using Chaos Enhanced Differential Evolution For Loss Minimization With Improvement of Voltage Profile of Distribution Systems.- Fractal and Periodical Biological Antennas: Hidden Topologies in DNA, Wasps and Retina in the Eye.- Efficient Multiprocessor Scheduling Using Water Cycle Algorithm.- Estimating Software Reliability Growth Model Parameters Using Opposition Based Shuffled Frog-Leaping Algorithm.

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Book SynopsisThis book stems from a unique and a highly effective approach to introducing signal processing, instrumentation, diagnostics, filtering, control, system integration, and machine learning.It presents the interactive industrial grade software testbed of mold oscillator that captures the distortion induced by beam resonance and uses this testbed as a virtual lab to generate input-output data records that permit unravelling complex system behavior, enhancing signal processing, modeling, and simulation background, and testing controller designs.All topics are presented in a visually rich and mathematically well supported, but not analytically overburdened format. By incorporating software testbed into homework and project assignments, the narrative guides a reader in an easily followed step-by-step fashion towards finding the mold oscillator disturbance removal solution currently used in the actual steel production, while covering the key signal processing, control, system integration, and machine learning concepts.The presentation is extensively class-tested and refined though the six-year usage of the book material in a required engineering course at the University of Illinois at Urbana-Champaign.

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Book SynopsisThis book stems from a unique and a highly effective approach to introducing signal processing, instrumentation, diagnostics, filtering, control, system integration, and machine learning.It presents the interactive industrial grade software testbed of mold oscillator that captures the distortion induced by beam resonance and uses this testbed as a virtual lab to generate input-output data records that permit unravelling complex system behavior, enhancing signal processing, modeling, and simulation background, and testing controller designs.All topics are presented in a visually rich and mathematically well supported, but not analytically overburdened format. By incorporating software testbed into homework and project assignments, the narrative guides a reader in an easily followed step-by-step fashion towards finding the mold oscillator disturbance removal solution currently used in the actual steel production, while covering the key signal processing, control, system integration, and machine learning concepts.The presentation is extensively class-tested and refined though the six-year usage of the book material in a required engineering course at the University of Illinois at Urbana-Champaign.

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Book SynopsisThis book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.Table of ContentsIntroduction.- Preliminaries.- Methods of Estimation - Iterative.- Methods of Estimation - Non-iterative.- Asymptotic Results (of Sinusoidal model).- Order estimation.- Fundamental Frequency Model and its generalization.- Data Analysis.- Two dimensional and multidimensional models.- Chirp Signal Model.- Random Amplitudes.- Related Models.- Appendices.

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Book SynopsisThe focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal processing techniques that can be imagined. To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems.Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.Trade Review“This book is based on material that has been prepared for senior-level undergraduate classes, this book can be used for one-semester senior-level undergraduate and graduate-level classes.” (Arzu Ahmadova, zbMATH 1493.68003, 2022)Table of ContentsPart I Basic Tools for Machine Learning: 1. Mathematical Preliminaries.- 2. Linear and Kernel Classifiers.- 3. Linear, Logistic, and Kernel Regression.- 4. Reproducing Kernel Hilbert Space, Representer Theorem.- Part II Building Blocks of Deep Learning: 5. Biological Neural Networks.- 6. Artificial Neural Networks and Backpropagation.- 7. Convolutional Neural Networks.- 8. Graph Neural Networks.- 9. Normalization and Attention.- Part III Advanced Topics in Deep Learning.- 10. Geometry of Deep Neural Networks.- 11. Deep Learning Optimization.- 12. Generalization Capability of Deep Learning.- 13. Generative Models and Unsupervised Learning.- Summary and Outlook.- Bibliography.- Index.

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