Details

<p>Preface xix</p> <p>Acknowledgements xxiii</p> <p>Symbols xxv</p> <p>Abbreviations xxix</p> <p>1 Introduction 1</p> <p>2 Noise and Distortion 35</p> <p>3 Information Theory and Probability Models 51</p> <p>4 Bayesian Inference 107</p> <p>5 Hidden Markov Models 147</p> <p>6 Least Square Error Wiener-Kolmogorov Filters 173</p> <p>7 Adaptive Filters: Kalman, RLS, LMS 193</p> <p>8 Linear Prediction Models 227</p> <p>9 Eigenvalue Analysis and Principal Component Analysis 257</p> <p>10 Power Spectrum Analysis 271</p> <p>11 Interpolation – Replacement of Lost Samples 295</p> <p>12 Signal Enhancement via Spectral Amplitude Estimation 321</p> <p>13 Impulsive Noise: Modelling, Detection and Removal 341</p> <p>14 Transient Noise Pulses 359</p> <p>15 Echo Cancellation 371</p> <p>16 Channel Equalisation and Blind Deconvolution 391</p> <p>17 Speech Enhancement: Noise Reduction, Bandwidth Extension and Packet Replacement 423</p> <p>18 Multiple-Input Multiple-Output Systems, Independent Component Analysis 467</p> <p>19 Signal Processing in Mobile Communication 491</p> <p>Bibliography 508</p> <p>Index 509</p>

<b>SAEED V. VASEGHI</b>,<i> Brunel University, UK</i>

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. <p>The fourth edition of <i>Advanced Digital Signal Processing and Noise Reduction</i> 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.</p> <ul class="noindent"> <li>Two new chapters on MIMO systems, correlation and Eigen analysis and independent component analysis</li> </ul> <ul class="noindent"> <li>Comprehensive coverage of advanced digital signal processing and noise reduction methods for communication and information processing systems</li> </ul> <ul class="noindent"> <li>Examples and applications in signal and information extraction from noisy data</li> </ul> <ul class="noindent"> <li>Comprehensive but accessible coverage of signal processing theory including probability models, Bayesian inference, hidden Markov models, adaptive filters and Linear prediction models</li> </ul> <p><i>Advanced Digital Signal Processing and Noise Reduction</i> 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.</p>

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