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Godsill S.J., Rayner P.J.W. Digital Audio Restoration - a Statistical Model Based Approach

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Godsill S.J., Rayner P.J.W. Digital Audio Restoration - a Statistical Model Based Approach
Springer, 1998. — 346 pp.
Алгоритмы устранения дефектов (щелчков, шипений, низкочастотных шумов и т.д.) в аудиозаписях.
Until recently, however, digital audio processing has required high-powered computational engines which were only available to large institutions who could afford to use the sophisticated digital remastering technology. With the advent of compact disc and other digital audio formats, followed by the increased accessibility of home computing, digital audio processing is now available to anyone who owns a PC with sound card, and will be of increasing importance, in association with digital video, as the multimedia revolution continues into the next millennium. Digital audio restoration will thus find increasing application to sound recordings from the internet, home recordings and speech, and high-quality noise-reducers will become a standard part of any computer system and hifi system, alongside speech recognisers and image processors.
In this book we draw upon extensive experience in the commercial world of sound restoration1 and in the academic community, to give a comprehensive overview of the principles behind the current technology, as implemented in the commercial restoration systems of today. Furthermore, if the current technology can be regarded as a ‘first phase’ in audio restoration, then the later chapters of the book outline a ‘second phase’ of more sophisticated statistical methods which are aimed at achieving higher fidelity to the original recorded sound and at addressing problems which cannot
currently be handled by commercial systems. It is anticipated that new methods such as these will form the basis of future restoration systems.
Introduction
Fundamentals
Digital Signal Processing
Probability Theory and Random Processes
Parameter Estimation, Model Selection and Classification
Basic Restoration Procedures
Removal of Clicks
Hiss Reduction
Advanced Topics
Removal of Low Frequency Noise Pulses
Restoration of Pitch Variation Defects
A Bayesian Approach to Click Removal
Bayesian Sequential Click Removal
Implementation and Experimental Results for Bayesian Detection
Fully Bayesian Restoration using EM and MCMC
Summary and Future Research Directions
Probability Densities and Integrals
Matrix Inverse Updating Results and Associated Properties
Exact Likelihood for AR Process
Derivation of Likelihood for i
Marginalised Bayesian Detector
Derivation of Sequential Update Formulae
Derivations for EM-based Interpolation
Derivations for Gibbs Sampler
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