Non Gaussianity and Non Stationarity modeled through Hidden Variables and their use in ICA and Blind Source Separation

TL;DR

This paper introduces new hidden variable-based models for non-Gaussian and non-stationary signals, enhancing ICA and BSS techniques with Bayesian estimation methods.

Contribution

It proposes novel models for complex signals and demonstrates their application in ICA and BSS, along with Bayesian computational approaches.

Findings

New models for stationary non-Gaussian signals

Models for Gaussian non-stationary signals

Application of models in ICA and BSS

Abstract

Modeling non Gaussian and non stationary signals and images has always been one of the most important part of signal and image processing methods. In this paper, first we propose a few new models, all based on using hidden variables for modeling either stationary but non Gaussian or Gaussian but non stationary or non Gaussian and non stationary signals and images. Then, we will see how to use these models in independent component analysis (ICA) or blind source separation (BSS). The computational aspects of the Bayesian estimation framework associated with these prior models are also discussed.