Godsill, SJ (1997) Robust modelling of noisy ARMA signals. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 5. pp. 3797-3800. ISSN 0736-7791Full text not available from this repository.
In this paper methods are developed for enhancement and analysis of autoregressive moving average (ARMA) signals observed in additive noise which can be represented as mixtures of heavy-tailed non-Gaussian sources and a Gaussian background component. Such models find application in systems such as atmospheric communications channels or early sound recordings which are prone to intermittent impulse noise. Markov Chain Monte Carlo (MCMC) simulation techniques are applied to the joint problem of signal extraction, model parameter estimation and detection of impulses within a fully Bayesian framework. The algorithms require only simple linear iterations for all of the unknowns, including the MA parameters, which is in contrast with existing MCMC methods for analysis of noise-free ARMA models. The methods are illustrated using synthetic data and noise-degraded sound recordings.
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron job|
|Date Deposited:||16 Jul 2015 13:52|
|Last Modified:||04 Oct 2015 04:34|