SEMI-BLIND SOURCE SEPARATION FOR ESTIMATION OF CLAY CONTENT OVER SEMI-VEGETATED AREAS, FROM VNIR/SWIR HYPERSPECTRAL AIRBORNE DATA

Semi-Blind Source Parameter Separation

Independent Component Analysis (ICA) is a useful extension of standard Principal Component Analysis (PCA). The ICA model is utilized mainly in blind separation of unknown source signals from their linear mixtures. In some applications, the mixture coeecients are totally unknown, while some knowledge about temporal model exists. In this paper, we propose a learning system for semi-blind binary s...

Blind and semi-blind source separation

A fast semi-blind source separation algorithm

Many algorithms have been invented to perform blind source separation. There, it is assumed that the sources are completely unknown. However, in many cases of time series separation there exists some a priori knowledge on the behaviour of the sources. This is the case especially, when the data is collected in a controlled experiment. In this paper we introduce a fast semi-blind source separatio...

Acoustical Semi-blind Source Separation for Machine Monitoring

In this contribution, convolutive blind source separation algorithms are compared with the well studied theory of minimum variance beamforming. As a result, the equivalence between the delay vector in the PCA (principle component analysis) subspace and the column the of rotation matrix belonging to the the target sound is shown. That equivalence yields a new semi-blind algorithm being more robu...

Semi-Blind Approaches for Source Separation and Independent component Analysis

This paper is a survey of semi-blind source separation approaches. Since Gaussian iid signals are not separable, simplest priors suggest to assume non Gaussian iid signals, or Gaussian non iid signals. Other priors can also been used, for instance discrete or bounded sources, positivity, etc. Although providing a generic framework for semi-blind source separation, Sparse Component Analysis and ...