In this paper, we propose two versions of a correlation-based blind source separation (BSS) method. Whereas its basic version operates in the time domain, its extended form is based on the timefrequency (TF) representations of the observed signals and thus applies to much more general conditions. The latter approach consists in identifying the columns of the (permuted scaled) mixing matrix in T...

Noise is an unavoidable factor in real sensor signals. We study how additive and convolutive noise can be reduced or even eliminated in the blind source separation (BSS) problem. Particular attention is paid to cases in which the number of sensors is larger than the number of sources. We propose various methods and associated adaptive learning algorithms for such an extended BSS problem. Perfor...

Independent Component Analysis (ICA) is a computational method to solve Blind Source Separation (BSS) problem. In this study, an improved Fast ICA based on eighth-order Newton’s method is proposed to solve BSS problems. Eight-order Newton’s method for finding the solution of nonlinear equations is much faster than ordinary Newton’s iterative method. The improved FastICA algorithm is applied to ...

Nowadays, Blind Source Separation (BSS) techniques are very common and useful in signal processing. In the field of multichannel recording, there are many techniques of BSS that work accurately, but in the single channel measurement, only a few methods are existed. One of the much popular algorithms of BSS is Independent Component Analysis (ICA). This technique is applied to separate the indepe...

Current methods in Blind Source Separation (BSS) utilize either the higher order statistics or the time delayed crosscorrelations to perform signal separation. In this paper we investigate a method for source separation which utilizes joint information from higher order statistics and delayed cross-correlations. The algorithm is motivated by problems in analysis of Electroencephalography (EEG) ...

Blind source separation (BSS) for convolutive mixtures can be performed efficiently in the frequency domain, where independent component analysis (ICA) is applied separately in each frequency bin. However, frequencydomain BSS involves two major problems that must be solved. The first is the permutation problem: the permutation ambiguity of ICA should be aligned so that a separated signal in the...

Temporal BYY (TBYY) learning has been presented for modeling signal in a general state space approach, which provides not only a unified point of view on Kalman filter, hidden Markov model (HMM), independent component analysis (ICA), and blind source separation (BSS) with extensions, but also further advances on these studies, including a higher order HMM, independent HMM for binary BSS, tempor...

This paper addresses the problem of blind source separation (BSS). To recover original signals, from linear instantaneous mixtures, we propose a new contrast function based on the use of a double referenced system. Our approach assumes statistical independence sources. The reference vectors will be incrusted in the cumulant to evaluate the independence. The estimation of the separating matrix w...

In this paper, we describe a new blind source separation (BSS) method that uses spatial information derived from the direction of arrival (DOA) estimates of each direct and reflected sound. The method we proposed has the following steps: (1) each DOA is estimated using matching pursuit and re-optimized after each new DOA is estimated, (2) using these DOA estimates, the mixing matrix is also est...