Sciweavers

Share
JMLR
2006

Linear State-Space Models for Blind Source Separation

9 years 3 months ago
Linear State-Space Models for Blind Source Separation
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 quasiperiodicity, 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.
Rasmus Kongsgaard Olsson, Lars Kai Hansen
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JMLR
Authors Rasmus Kongsgaard Olsson, Lars Kai Hansen
Comments (0)
books