Speaker: Zois Boukouvalis

Title

An efficient multivariate generalized Gaussian distribution estimator: Application to IVA

Abstract

Due to its simple parametric form, multivariate Gaussian distribution (MGGD) has been widely used in modeling vector-valued signals. Therefore, efficient estimation of its parameters is of significant interest for a number of applications. Independent vector analysis (IVA) is a generalization of independent component analysis (ICA) that makes full use of the statistical dependence across multiple datasets to achieve source separation, and can take both second and higher-order statistics into account. MGGD provides an effective model for IVA as well to model the latent multivariate variables--sources--and the performance of the IVA algorithm highly depends on the estimation of the source parameters. In this work, we propose an efficient estimation technique based on the Fisher scoring (FS) and demonstrate its successful application to IVA. We quantify the performance of MGGD parameter estimation using FS and further verify the effectiveness of the new IVA algorithm using simulations.