Multimedia Signal Processing: Theory and Applications in Speech, Music and Communications

Transcription

1 Brochure More information from Multimedia Signal Processing: Theory and Applications in Speech, Music and Communications Description: Multimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network management, radar, pattern recognition, medical signal processing, financial data forecasting, artificial intelligence, decision making, control systems and search engines. This book is organised in to three major parts making it a coherent and structured presentation of the theory and applications of digital signal processing. A range of important topics are covered in basic signal processing, model-based statistical signal processing and their applications. The aim of this book is to provide a coherent and structured presentation of the theory and applications of statistical signal processing in three sections: Part 1: Basic Digital Signal Processing gives an introduction to the topic, discussing sampling and quantization, Fourier analysis and synthesis, Z-transform, and digital filters. Part 2: Model-based Signal Processing covers probability and information models, Bayesian inference, Wiener filter, adaptive filters, linear prediction hidden Markov models and independent component analysis. Part 3: Applications of Signal Processing in Speech, Music and Telecommunications explains the topics of speech and music processing, echo cancellation, deconvolution and channel equalization, and mobile communication signal processing. - Covers music signal processing, explains the anatomy and psychoacoustics of hearing and the design of MP3 music coder - Examines speech processing technology including speech models, speech coding for mobile phones and speech recognition - Covers single-input and multiple-inputs denoising methods, bandwidth extension and the recovery of lost speech packets in applications such as voice over IP (VoIP) - Illustrated throughout, including numerous solved problems, Matlab experiments and demonstrations - Companion website features Matlab and C++ programs with electronic copies of all figures. This book is ideal for researchers, postgraduates and senior undergraduates in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be a valuable text to professional engineers in telecommunications and audio and signal processing industries. About the author: Saeed Vaseghi is Professor of Communications and Signal Processing at Brunel Universitys Department of Electronics and Computer Engineering and is Group Leader for the Communications & Multimedia Signal Processing Group. Previously, Saeed obtained a first in Electrical and Electronics Engineering from Newcastle University, and a PhD in Digital Signal Processing from Cambridge University. His PhD in noisy signal restoration led to establishment of CEDAR, the world's leading system for restoration of audio signals. Saeed also held a British Telecom lectureship at UEA Norwich, and a readership at Queen's University of Belfast before his move to Brunel Contents: Preface Acknowledgement

2 Symbols Abbreviations Part I Basic Digital Signal Processing 1 Introduction 1.1 Signals and Information 1.2 Signal Processing Methods 1.3 Applications of Digital Signal Processing 1.4 Summary 2 Fourier Analysis and Synthesis 2.1 Introduction 2.2 Fourier Series: Representation of Periodic Signals 2.3 Fourier Transform: Representation of Nonperiodic Signals 2.4 Discrete Fourier Transform 2.5 Short-Time Fourier Transform 2.6 Fast Fourier Transform (FFT) D Discrete Fourier Transform (2-D DFT) 2.8 Discrete Cosine Transform (DCT) 2.9 Some Applications of the Fourier Transform 2.10 Summary 3 z-transform 3.1 Introduction 3.2 Derivation of the z-transform 3.3 The z-plane and the Unit Circle 3.4 Properties of z-transform 3.5 z-transfer Function, Poles (Resonance) and Zeros (Anti-resonance) 3.6 z-transform of Analysis of Exponential Transient Signals 3.7 Inverse z-transform 3.8 Summary 4 Digital Filters 4.1 Introduction 4.2 Linear Time-Invariant Digital Filters 4.3 Recursive and Non-Recursive Filters 4.4 Filtering Operation: Sum of Vector Products, A Comparison of Convolution and Correlation 4.5 Filter Structures: Direct, Cascade and Parallel Forms 4.6 Linear Phase FIR Filters 4.7 Design of Digital FIR Filter-banks 4.8 Quadrature Mirror Sub-band Filters 4.9 Design of Infinite Impulse Response (IIR) Filters by Pole zero Placements 4.10 Issues in the Design and Implementation of a Digital Filter 4.11 Summary 5 Sampling and Quantisation 5.1 Introduction 5.2 Sampling a Continuous-Time Signal 5.3 Quantisation 5.4 Sampling Rate Conversion: Interpolation and Decimation 5.5 Summary Part II Model-Based Signal Processing 6 Information Theory and Probability Models 6.1 Introduction: Probability and Information Models

3 6.2 Random Processes 6.3 Probability Models of Random Signals 6.4 Information Models 6.5 Stationary and Non-Stationary Random Processes 6.6 Statistics (Expected Values) of a Random Process 6.7 Some Useful Practical Classes of Random Processes 6.8 Transformation of a Random Process 6.9 Search Engines: Citation Ranking 6.10 Summary 7 Bayesian Inference 7.1 Bayesian Estimation Theory: Basic Definitions 7.2 Bayesian Estimation 7.3 Expectation Maximisation Method 7.4 Cramer Rao Bound on the Minimum Estimator Variance 7.5 Design of Gaussian Mixture Models (GMM) 7.6 Bayesian Classification 7.7 Modelling the Space of a Random Process 7.8 Summary 8 Least Square Error, Wiener Kolmogorov Filters 8.1 Least Square Error Estimation: Wiener Kolmogorov Filter 8.2 Block-Data Formulation of the Wiener Filter 8.3 Interpretation of Wiener Filter as Projection in Vector Space 8.4 Analysis of the Least Mean Square Error Signal 8.5 Formulation of Wiener Filters in the Frequency Domain 8.6 Some Applications of Wiener Filters 8.7 Implementation of Wiener Filters 8.8 Summary 9 Adaptive Filters: Kalman, RLS, LMS 9.1 Introduction 9.2 State-Space Kalman Filters 9.3 Sample Adaptive Filters 9.4 Recursive Least Square (RLS) Adaptive Filters 9.5 The Steepest-Descent Method 9.6 LMS Filter 9.7 Summary 10 Linear Prediction Models 10.1 Linear Prediction Coding 10.2 Forward, Backward and Lattice Predictors 10.3 Short-Term and Long-Term Predictors 10.4 MAP Estimation of Predictor Coefficients 10.5 Formant-Tracking LP Models 10.6 Sub-Band Linear Prediction Model 10.7 Signal Restoration Using Linear Prediction Models 10.8 Summary 11 Hidden Markov Models 11.1 Statistical Models for Non-Stationary Processes 11.2 Hidden Markov Models 11.3 Training Hidden Markov Models 11.4 Decoding Signals Using Hidden Markov Models 11.5 HMM in DNA and Protein Sequences 11.6 HMMs for Modelling Speech and Noise 11.7 Summary

4 12 Eigenvector Analysis, Principal Component Analysis and Independent Component Analysis 12.1 Introduction Linear Systems and Eigenanalysis 12.2 Eigenvectors and Eigenvalues 12.3 Principal Component Analysis (PCA) 12.4 Independent Component Analysis 12.5 Summary Part III Applications of Digital Signal Processing to Speech, Music and Telecommunications 13 Music Signal Processing and Auditory Perception 13.1 Introduction 13.2 Musical Notes, Intervals and Scales 13.3 Musical Instruments 13.4 Review of Basic Physics of Sounds 13.5 Music Signal Features and Models 13.6 Anatomy of the Ear and the Hearing Process 13.7 Psychoacoustics of Hearing 13.8 Music Coding (Compression) 13.9 High Quality Audio Coding: MPEG Audio Layer-3 (MP3) Stereo Music Coding Summary 14 Speech Processing 14.1 Speech Communication 14.2 Acoustic Theory of Speech: The Source filter Model 14.3 Speech Models and Features 14.4 Linear Prediction Models of Speech 14.5 Harmonic Plus Noise Model of Speech 14.6 Fundamental Frequency (Pitch) Information 14.7 Speech Coding 14.8 Speech Recognition 14.9 Summary 15 Speech Enhancement 15.1 Introduction 15.2 Single-Input Speech Enhancement Methods 15.3 Speech Bandwidth Extension Spectral Extrapolation 15.4 Interpolation of Lost Speech Segments Packet Loss Concealment 15.5 Multi-Input Speech Enhancement Methods 15.6 Speech Distortion Measurements 15.7 Summary 16 Echo Cancellation 16.1 Introduction: Acoustic and Hybrid Echo 16.2 Telephone Line Hybrid Echo 16.3 Hybrid (Telephone Line) Echo Suppression 16.4 Adaptive Echo Cancellation 16.5 Acoustic Echo 16.6 Sub-Band Acoustic Echo Cancellation 16.7 Echo Cancellation with Linear Prediction Pre-whitening 16.8 Multi-Input Multi-Output Echo Cancellation 16.9 Summary 17 Channel Equalisation and Blind Deconvolution 17.1 Introduction 17.2 Blind Equalisation Using Channel Input Power Spectrum 17.3 Equalisation Based on Linear Prediction Models

5 17.4 Bayesian Blind Deconvolution and Equalisation 17.5 Blind Equalisation for Digital Communication Channels 17.6 Equalisation Based on Higher-Order Statistics 17.7 Summary 18 Signal Processing in Mobile Communication 18.1 Introduction to Cellular Communication 18.2 Communication Signal Processing in Mobile Systems 18.3 Capacity, Noise, and Spectral Efficiency 18.4 Multi-path and Fading in Mobile Communication 18.5 Smart Antennas Space Time Signal Processing 18.6 Summary Index Ordering: Order Online - Order by Fax - using the form below Order by Post - print the order form below and sent to Research and Markets, Guinness Centre, Taylors Lane, Dublin 8, Ireland.

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