Conventional Blind Source Separation (BSS) algorithms separate the sources assuming the number of sources equals to that of observations. BSS algorithms have been developed based on an assumption that all sources have non-Gaussian distributions. Most of the instances, these algorithms separate speech signals with super-Gaussian distributions. However, in real world examples there exist speech s...

The TRINICON (‘Triple-N ICA for convolutive mixtures’) framework is an effective blind signal separation (BSS) method for separating sound sources from convolutive mixtures. It makes full use of the non-whiteness, non-stationarity and nonGaussianity properties of the source signals and can be implemented either in time domain or in frequency domain, avoiding the notorious internal permutation p...

We address the problem of passive blind estimation of time-delays for several mutually uncorrelated source signals received by a similar number of sensors. The mixtures at the receivers are modeled as unknown linear combinations of differently delayed versions of the source signals. The standard tools used in blind source separation (BSS) for either static or convolutive mixtures are inappropri...

Blind source separation (BSS) is a relatively recent technique, more and more applied in electroencephalographic (EEG) signal processing. Still, the classical mixing model of the BSS does not take into account the real recording set-up. In fact, a major problem in electrophysiological recording systems (e.g. ECG, EEG, EMG) is to find a region in the human body whose bio-potential activity can b...

This paper studies the problem of blind separation of convolutively mixed source signals on the basis of the joint diagonalization (JD) of power spectral density matrices (PSDMs) observed at the output of the separation system. Firstly, a general framework of JD-based blind source separation (BSS) is reviewed and summarized. Special emphasis is put on the separability conditions of sources and ...

Blind source separation (BSS) is a technique for recovering a set of source signals without a priori information on the transformation matrix or the probability distributions of source signals. Based on separation results of outputs, this paper proposes the interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC)-based learning rate adjustment for the BSS. The adopted T2FCMAC sy...

This paper proposes the fusion of two important paradigms, Genetic Algorithms and the Blind Separation of Sources in Nonlinear Mixtures (GABSS). Although the topic of BSS, by means of various techniques, including ICA, PCA, and neural networks, has been amply discussed in the literature, the possibility of using genetic algorithms has not been explored thus far. However, in Nonlinear Mixtures, ...

Recently, the concept of time-frequency masking has developed as an important approach to the blind source separation problem, particularly when in the presence of reverberation. However, previous research has been limited by factors such as the sensor arrangement and/or the mask estimation technique implemented. This paper presents a novel integration of two established approaches to BSS in an...

In this paper, we consider an extension of independent component analysis (ICA) and blind source separation (BSS) techniques to several related data sets. The goal is to separate mutually dependent and independent components or source signals from these data sets. This problem is important in practice, because such data sets are common in real-world applications. We propose a new method which f...

ABSTRACT For several years, contrast-based Blind Source Separation (BSS) has been successfully used in several areas, including radiocommunications. Here a functional approach relying on differential calculus theory is proposed, aiming at analyzing asymptotic performances of BBS contrast criteria: the variance of the estimated separating matrix is expressed as a function of that of estimated cu...