This paper develops novel approaches in this direction based on the principles of maximum likelihood and minimum mutual information. These principles are ...

We formulate a spatial non-stationary blind source separation model and provide three different estimators that are based on the joint diagonalization of ...

Most blind sources separation methods are based on the non Gaussianity or the coloration of the sources and only recently their non-stationarity.

The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We ...

Jul 5, 2021 · Recently, for that purpose spatial blind source separation (SBSS) was introduced which assumes that the observed data are formed by a linear ...

Abstract. In a previous work, we developed a quasi-efficient maximum likelihood approach for blindly separating stationary, temporally corre- lated sources ...

Blind source separation (BSS) has attained much attention in signal processing society due to its 'blind' property and wide applications.

Blind source separation can be achieved by exploiting nonGaussianity, time cor- relation or nonstationarity [1]. In this paper, our goal is to propose new ap-.

In this paper, we propose a new approach for blind separation of noisy, over-determined, linear instantaneous mixtures of non-stationary sources.

Dec 1, 2006 · Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source ...