Godsill, SJ and Andrieu, C (1999) Bayesian separation and recovery of convolutively mixed autoregressive sources. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 3. pp. 1733-1736. ISSN 0736-7791Full text not available from this repository.
In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||18 May 2016 19:10|
|Last Modified:||27 May 2016 00:38|