PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Linear State-space Models for Blind Source Separation
R. K. Olsson and L. K. Hansen
Journal of Machine Learning Research 2006.


We apply a type of generative modelling to the problem of blind source separation in which prior knowledge about the latent source signals, such as time-varying auto-correlation and quasi-periodicity, are incorporated into a linear state-space model. In simulations, we show that in terms of signal-to-error ratio, the sources are inferred more accurately as a result of the inclusion of strong prior knowledge. We explore different schemes of maximum-likelihood optimization for the purpose of learning the model parameters. The Expectation Maximization algorithm, which is often considered the standard optimization method in this context, results in slow convergence when the noise variance is small. In such scenarios, quasi-Newton optimization yields substantial improvements in a range of signal to noise ratios. We analyze the performance of the methods on convolutive mixtures of speech signals.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2694
Deposited By:Rasmus Olsson
Deposited On:22 November 2006