Identifiability for Blind Source Separation of Multiple Finite Alphabet Linear Mixtures

Neural Network Based Blind Source Separation of Non-linear Mixtures

In this paper we present a novel neural network topology capable of separating simultaneous signals transferred through a memoryless non-linear path. The employed neural network is a two-layer perceptron that uses parametric non-linearities in the hidden neurons. The non-linearities are formed using a mixture of sigmoidal non-linear functions and present greater adaptation towards separating co...

Blind Source Separation of Linear Mixtures with Singular Matrices

Abstract. We consider the Blind Source Separation problem of linear mixtures with singular matrices and show that it can be solved if the sources are sufficiently sparse. More generally, we consider the problem of identifying the source matrix S ∈ IR if a linear mixture X = AS is known only, where A ∈ IR, m 6 n and the rank of A is less than m. A sufficient condition for solving this problem is...

Blind Source Separation of Overdetermined Linear-Quadratic Mixtures

This work deals with the problem of source separation in overdetermined linear-quadratic (LQ) models. Although the mixing model in this situation can be inverted by linear structures, we show that some simple independent component analysis (ICA) strategies that are often employed in the linear case cannot be used with the studied model. Motivated by this fact, we consider the more complex yet m...

Blind Source Separation of Single Components from Linear Mixtures

Convolutive Blind Source Separation for Noisy Mixtures

The problem of separating convolutive mixtures of unknown time series arises in several application domains, a prominent example being the so-called cocktail party problem, where we want to recover the speech signals of multiple speakers who are simultaneously talking in a room. The room may be reverberant due to reflections on the walls, i.e., the original source signals sq(n), q = 1, . . . , ...