OBJECTIVE The contamination of muscle and eye artifacts during an ictal period of the EEG significantly distorts source estimation algorithms. Recent blind source separation (BSS) techniques based on canonical correlation (BSS-CCA) and independent component analysis with spatial constraints (SCICA) have shown much promise in the removal of these artifacts. In this study we want to use BSS-CCA a...

The determination of the number (n) of unobserved sources is an important issue in Blind Source Separation (BSS) of linear and instantaneous mixtures. However BSS is already a difficult task, so we generally assume that this number (n) is known and a priori fixed. In this paper, we address this issue as a Bayesian model selection problem and view the determination of this number (n) as a hypoth...

In this paper we present a simple method to deal with Blind Source Separation (BSS) of Finite Impulse Response (FIR) convolutive mixtures. The global method proceeds in two steps. The first step consists in separating each source contribution in the mixture. This step provides several filtered version of each source. The second step consists in retrieving the original sources from the set of fi...

In the present work a novel algorithmic rule by taking the speech from two different microphones and separate these speeches by prediction of separating speech mixtures that is predicated on separation matrices is planned. In multitalker applications so as to boost individual speech sources from their mixtures is done by Blind source Separation (BSS) ways. From the previous published works of s...

We investigate novel algorithms to improve the convergence and reduce the complexity of time-domain convolutive blind source separation (BSS) algorithms. First, we propose MMax partial update time-domain convolutive BSS (MMax BSS) algorithm. We demonstrate that the partial update scheme applied in the MMax LMS algorithm for single channel can be extended to multichannel time-domain convolutive ...

Frequency domain blind source separation (BSS) problems are typically solved in each frequency bin independently and therefore require additional measures to resolve the resulting permutation problem. In this paper, a frequency domain methodology is presented based on a recently introduced extension of Independent Component Analysis (ICA) to multi-variate components which uses a multi-variate a...

The problem of blind source separation (BSS) of convolved acoustic signals is of great interest for many classes of applications. Due to the convolutive mixing process, the source separation is performed in the frequency domain, using independent component analysis (ICA). However, frequency domain BSS involves several major problems that must be solved. One of these is the permutation problem. ...

We present an approach to simultaneously separate and reconstruct signals from a compressively sensed linear mixture. We assume that the signals have a common sparse representation. The approach combines classical Compressive Sensing (CS) theory with a linear mixing model. Since Blind Source Separation (BSS) from a linear mixture is only possible up to permutation and scaling, factoring out the...

This thesis addresses the problem of blind signal separation (BSS) using independent component analysis (ICA). In blind signal separation, signals from multiple sources arrive simultaneously at a sensor array, so that each sensor array output contains a mixture of source signals. Sets of sensor outputs are processed to recover the source signals or to identify the mixing system. The term blind ...

Introduction Blind source separation (BSS) refers to the problem of recovering signals from several observed linear mixtures (e.g., [1]). In this paper we deal with the convolutive mixing case as encountered, e.g., in acoustic environments, and aim at finding a corresponding demixing system, whose output signals yq(n), q = 1, . . . , P are described by yq(n) = ∑P p=1 ∑L−1 κ=0 wpq,κxp(n−κ), and ...