Summary and Info
Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system. The fourth edition of Advanced Digital Signal Processing and Noise Reduction updates and extends the chapters in the previous edition and includes two new chapters on MIMO systems, Correlation and Eigen analysis and independent component analysis. The wide range of topics covered in this book include Wiener filters, echo cancellation, channel equalisation, spectral estimation, detection and removal of impulsive and transient noise, interpolation of missing data segments, speech enhancement and noise/interference in mobile communication environments. This book provides a coherent and structured presentation of the theory and applications of statistical signal processing and noise reduction methods. Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysis Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems Examples and applications in signal and information extraction from noisy data Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction models Advanced Digital Signal Processing and Noise Reduction is an invaluable text for postgraduates, senior undergraduates and researchers in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be of interest to professional engineers in telecommunications and audio and signal processing industries and network planners and implementers in mobile and wireless communication communities.Content: Chapter 1 Introduction (pages 1–33): Chapter 2 Noise and Distortion (pages 35–50): Chapter 3 Information Theory and Probability Models (pages 51–105): Chapter 4 Bayesian Inference (pages 107–146): Chapter 5 Hidden Markov Models (pages 147–172): Chapter 6 Least Square Error Wiener?Kolmogorov Filters (pages 173–191): Chapter 7 Adaptive Filters: Kalman, RLS, LMS (pages 193–225): Chapter 8 Linear Prediction Models (pages 227–255): Chapter 9 Eigenvalue Analysis and Principal Component Analysis (pages 257–270): Chapter 10 Power Spectrum Analysis (pages 271–294): Chapter 11 Interpolation – Replacement of Lost Samples (pages 295–320): Chapter 12 Signal Enhancement via Spectral Amplitude Estimation (pages 321–339): Chapter 13 Impulsive Noise: Modelling, Detection and Removal (pages 341–358): Chapter 14 Transient Noise Pulses (pages 359–369): Chapter 15 Echo Cancellation (pages 371–390): Chapter 16 Channel Equalisation and Blind Deconvolution (pages 391–421): Chapter 17 Speech Enhancement: Noise Reduction, Bandwidth Extension and Packet Replacement (pages 423–466): Chapter 18 Multiple?Input Multiple?Output Systems, Independent Component Analysis (pages 467–490): Chapter 19 Signal Processing in Mobile Communication (pages 491–508):
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