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