This paper addresses the application of blind source separation (BSS) algorithms to the problem of blind equalization (BE) in a frequency selective multiuser environment. Compared with other algorithms that exploit the structural properties of the channel or data matrix, our approaches are insensitive to channel order estimation, robust to ill-conditioned channels and able to choose the \best" ...

Source extraction in audio is an important problem in the study of blind source separation (BSS) with many practical applications. It is a challenging problem when the foreground sources to be extracted are weak compared to the background sources. Traditional techniques often do not work in this setting. In this paper we propose a novel technique for extracting foreground sources. This is achie...

In a recent paper, we proposed a new blind source separation (BSS) method, which uses timefrequency (TF) information to extract two source signals from two linear instantaneous mixtures of these sources. In this new paper, we introduce an extension of the latter method, intended for the general situation when mixtures of source signals are available. Unlike previously reported TF BSS methods, t...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

Blind source separation is an important but highly challenging technology in astronomy, physics, chemistry, life science, medical science, earth science, and applied sciences. Independent Component Analysis (ICA) employed technologies in applied computer science for blind source separation. In the separation of blind sources under multiple sensors, it can estimate approximately the types of sig...

| Separation of sources consists in recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: the linear mixture should bèblindly' processed. This typically occurs in narrow-band array processing applications when the array manifold is unknown or distorted. This paper introduces a new sour...

Blind source separation (BSS) and related topics such as independent component analysis (ICA), sparse component analysis (SCA), or nonnegative matrix factorization (NMF) have become emerging tools inmultivariate signal processing and data analysis and are now one of the hottest and emerging areas in signal processing with solid theoretical foundations and many potential applications. In fact, B...

Independent Component Analysis (ICA) is a useful extension of standard Principal Component Analysis (PCA). The ICA model is utilized mainly in blind separation of unknown source signals from their linear mixtures. In some applications, the mixture coeecients are totally unknown, while some knowledge about temporal model exists. In this paper, we propose a learning system for semi-blind binary s...

Blind source separation technique separates mixed signals blindly without any information on the mixing system. In this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. In these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. In order to evalu...

This paper presents a blind source separation process for convolutive mixtures of audio sources. Here undetermined condition that is few microphones than sources has been considered as a mixing model. By an expectation–maximization (EM) algorithm the separation operation is performed in the frequency domain. The T-F masking separation is made use which is a powerful approach for the separation ...