the infomax algorithm is a popular method in blind source separation problem. in this article an extension of the infomax algorithm is proposed that is able to separate mixed signals with any sub- or super-gaussian distributions. this ability is the results of using two different nonlinear functions and new coefficients in the learning rule. in this paper we show how we can use the distribution...

Article history: Available online 15 May 2012

This paper deals with the problem of Blind Source Separation. Contrary to the vast majority of works, we do not assume the statistical independence betwwen sources and explicitly consider that they are dependent. We introduce three particular models of dependent sources and show that their cumulants have interesting properties. Based on these properties, we investigate the behaviour of classica...

This paper presents and discusses the application of blind source separation to astrophysical data obtained with the WMAP satellite.

In this paper, we investigate the importance of the high frequencies in the problem of convolutive blind source separation (BSS) of speech signals. In particular, we focus on frequency domain blind source separation (FD-BSS), and show that when separation is performed in the low frequency bins only, the recovered signals are similar in quality to those extracted when all frequencies are taken i...