Adaptive Methods for Score Function Modeling in Blind Source Separation

摘要

In Blind Source Separation (BSS) the goal is to recover the source signals from the observed mixed signals (mixtures). Blindness means that neither the sources nor the mixing system is known. Separation can be based on the theoretically limiting but practically feasible assumption that the sources are statistically independent. The assumption connects BSS and Independent component Analysis (ICA). The usage of mutual information as a measure of independence leads to iterative estimation of the score functions of the mixtures. The purpose of this thesis is to develop BSS methods that can adapt to different source distributions.