This paper considers the blind separation of nonstationary sources in the underdetermined
case, when there are more sources than sensors. A general framework for this problem is to …

We examine the problem of blind separation of nonstationary sources in the
underdetermined case, where there are more sources than sensors. Since time-frequency …

This paper deals with the problem of blind source separation of nonstationary signals of
which only instantaneous linear signals are observed. Exploiting the effectiveness of time …

Recently, Aissa-El-Bey et al. have proposed two subspace-based methods for
underdetermined blind source separation (UBSS) in time-frequency (TF) domain. These …

In this paper, we propose a new blind source separation (BSS) method called TIme–
Frequency Ratio Of Mixtures (TIFROM) which uses time–frequency (TF) information to …

Blind source separation consists of recovering a set of signals of which only instantaneous
linear mixtures are observed. Thus far, this problem has been solved using statistical …

This paper considers the blind separation of nonstationary sources in the underdetermined
convolutive mixture case. We introduce, two methods based on the sparsity assumption of …

This paper deals with the blind separation of instantaneous mixtures of source signals using
time-frequency distributions (TFDs). We propose iterative algorithms to perform the …

This paper considers the problem of blindly separating sub-and super-Gaussian sources
from underdetermined mixtures. The underlying sources are assumed to be composed of …

A new approach for convolutive blind source separation (BSS) by explicitly exploiting the
second-order nonstationarity of signals and operating in the frequency domain is proposed …