Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity

Temporal and Time-frequency Correlation-based Blind Source Separation Methods

In this paper, we propose two versions of a correlation-based blind source separation (BSS) method. Whereas its basic version operates in the time domain, its extended form is based on the timefrequency (TF) representations of the observed signals and thus applies to much more general conditions. The latter approach consists in identifying the columns of the (permuted scaled) mixing matrix in T...

Blind Source Separation Using Temporal Predictability

A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals. It is shown that this propert...

Semi-blind source extraction for fetal electrocardiogram extraction by combining non-Gaussianity and time-correlation

Fetal electrocardiogram (FECG) extraction is a vital issue in biomedical signal processing and analysis. A promising approach is blind (semi-blind) source extraction. In this paper, we develop an objective function for extraction of temporally correlated sources. The objective function is based on the non-Gaussianity and the autocorrelations of source signals, and it contains the well-known mea...

Temporal Models in Blind Source Separation

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

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 t...