Simulation Methods for Linear and Nonlinear Time Series Models with Application to Distorted Audio Signals (1999)
Abstract / truncated to 115 words
This dissertation is concerned with the development of Markov chain Monte Carlo (MCMC) methods for the Bayesian restoration of degraded audio signals. First, the Bayesian approach to time series modelling is reviewed, then established MCMC methods are introduced. The first problem to be addressed is that of model order uncertainty. A reversible-jump sampler is proposed which can move between models of different order. It is shown that faster convergence can be achieved by exploiting the analytic structure of the time series model. This approach to model order uncertainty is applied to the problem of noise reduction using the simulation smoother. The effects of incorrect autoregressive (AR) model orders are demonstrated, and a mixed model order ...
Information
- Author
- Troughton, Paul Thomas
- Institution
- University of Cambridge
- Supervisor
- Publication Year
- 1999
- Upload Date
- July 3, 2008
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