In this paper, we consider independence property between a random process and its first derivative. Then, for linear mixtures, we show that cross-correlations between mixtures and...
Sebastien Lagrange, Luc Jaulin, Vincent Vigneron, ...
This contribution describes a neural network that self-organizes to recover the underlying original sources from typical sensor signals. No particular information is required abou...
In this paper we employ information theoretic algorithms, previously used for separating instantaneous mixtures of sources, for separating convolved mixtures in the frequency doma...
This paper presents a learning framework for blind source separation (BSS), in which the BSS is formulated as generalized Eigenvalue (GE) problem. Compared to the typical informati...
In this paper, we propose to use the scaling ambiguity of convolutive blind source separation for shortening the unmixing filters. An often used approach for separating convoluti...