Signal Processing Methods for Audio, Images and Telecommunications

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In recent years, rapid advances in computer hardware technology, including the development of specialized digital signal processors, have facilitated the development of algorithms whose applications would have been unthinkable only a short time ago. These algorithms allow for real-time application, make use of prior knowledge, can adapt in response to a changing environment, and are designed to achieve near-optimum performance under a broad range of operating conditions. This book examines the application of such algorithms to audio, video, and telecommunications.

The book is divided into four parts: methods, applications to audio, video, and telecommunications. Topics covered include wavelet transforms, adaptive filter design, neural networks, order statistic filters and projection methods.

Each chapter has been written by a leading expert in the field. Signal Processing Methods for Audio, Images and Telecommunications will be of great interest to students, researchers, and engineers alike, in all areas of signal and image processing.

Key Features
* Wavelet transforms
* Adaptive filter design
* Neural networks
* Order statistic filters
* Projections methods

Audience: Advanced students, researchers, and engineers in the areas of telecommunications, digital signal processing, image processing, acoustics and applied mathematics.

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Editorial Reviews

From the Publisher
This book can be of great value and interest to graduate students, researchers and engineers in all areas of signal and image processing. It can also be a valuable reference book for final year undergraduates engaged in signal processing projects. I found the book rich and compact, and a very special and useful signal processing book.
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Product Details

Table of Contents

Orthogonal Wavelets and Signal Processing: Multiresolution Analysis. Two Orthogonal Wavelet Bases. Discrete Wavelets and the Daubechies Construction. The Discrete Wavelet Transform Algorithm. Wavelets and Multiscale EdgeDetection. Wavelets and Non-Stationary Signal Analysis. Signal Compression Using Wavelets. Adaptive Filtering Using Vector Spaces of Systems: Fundamentals of Adaptive Filtering. The Vector Space Adaptive Filter. Algorithm Convergence and AsymptoticPerformance. Choosing the Vector Space. Choosing the Basis. Examples. Order Statistics and Adaptive Filtering: Median and Order Statistic Filters. Adaptive Filters and Order Statistics. OS Filters and Robustness. Rapid Adaptation--An Ad-Hoc Estimator. Multi-Layer Perceptron Neural Networks with Application to Speech Recognition: The Multi-Layer Perceptron. Signal Classification Design Examples. Perceptron Architecture and Learning. Statistical Training of Multi-Layer Perceptrons. Combining Multi-Layer Perceptrons for Speech Recognition. Auditory Localization Using Spectral Information: A Localization Model Based on HRTFs. Template Matching and a Matching Measure. Normalized Correlation Matching. Optimal DMM Matching. Matching Using Backpropagation Neural Networks. Experiments. Signal Processing by Projection Methods: Applications to Color Matching, Resolution Enhancement, and Blind Deconvolution: The Method of Projections Onto Convex Sets. Color Matching Problems. Resolution Enhancement. Generalized Projections. Projection-Based Blind Deconvolution. Projection Based Image Reconstruction from Compressed Data: Principles of the Proposed Recovery Approach. Constraint Set Based on the Transmitted Data. Constraint Sets Based on Prior Knowledge. The Recovery Algorithm. A Simplified Algorithm. Computational Complexity Analysis. Experiments. Non-Orthogonal Expansion for Template Matching and Edge Detection: The Correlation Approach: A Brief Review. The Discriminative Signal-to-Noise Ratio and Expansion Matching. EXM Related to Minimum Squared Error Restoration. EXM Related to Non-Orthogonal Expansion. Experimental Results. EXM-based Optimal DSNR Edge Detection. Locally Optimum Detection and Its Application to Communicationsand Signal Processing: Derivation of the Memoryless Locally Optimum Detector. Derivation of the Locally Optimum Detector with Memory. Detector Implementation Methods. LO Detection Applied to a Spread Spectrum System. Estimation of Probability Density Functions Using Projections Onto Convex Sets: Constraint Sets for Probability Density Function Estimation. Determining the Parameters in Constraint Sets. Competing Algorithms. Numerical Results. Subject Index.
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