We propose a time-domain BSS algorithm that utilizes geometric information such as sensor positions and assumed locations of sources. The algorithm tackles the problem of convolved mixtures by explicitly exploiting the non-stationarity of the acoustic sources. The learning rule is based on secondorder statistics and is derived by natural gradient minimization. The proposed initialization of the...

Blind source separation (BSS) and independent component analysis (ICA) are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to neuroscience. A recent trend in BSS is to consider problems in the framework of matrix factorization or more general signals decomposition with probabilistic generative and tree stru...

The match between the physics of MEG and the assumptions of the most well developed blind source separation (BSS) algorithms (unknown instantaneous linear mixing process, many sensors compared to expected recoverable sources, large data limit) have tempted researchers to apply these algorithms to MEG data. We review some of these efforts, with particular emphasis on our own work.

In this paper we present an approach capable of countering the presence of multiple access interference (MAI) in code division multiple access (CDMA) channels. We develop and implement a blind multiuser detector, based on an independent component analysis (ICA) to mitigate both MAI and noise. This algorithm has been utilized in blind source separation (BSS) of unknown sources from their linear ...

Independent component analysis (ICA) is a computational method to solve blind source separation (BSS) problem. In this paper, an improved FastICA based on Kurtosis was proposed to separate EEG signal. It was adopted from the gradient descent algorithm to improve the converge rate. Improved FastICA method was applied to separate EEG signal from a patient who had epilepsy seizure. The separation ...

Pharmacological FMRI in humans involves BOLD signal acquisition before, during and after the administration of a drug, and often results in a heterogeneous pattern of drug-induced hemodynamic responses in the brain. Exploratory techniques, including blind source separation, can be useful for BOLD data that contains patterns of cross-dependencies. Bayesian source separation (BSS) is a multivaria...

Blind source separation (BSS) can be used to separate an interfering source from a set of desired signals. By exploiting the statistical independence between the source signals and the interference, we present an alternative to the blind beamformer for suppressing the interference. [1, 2] presented the concept of combining Independent Component Analysis (ICA) with a conventional detection for s...

This paper focuses on the underdetermined blind source separation (BSS) of three speech signals mixed in a real environment from measurements provided by two sensors. To date, solutions to the underdetermined BSS problem have mainly been based on the assumption that the speech signals are sufficiently sparse. They involve designing binary masks that extract signals at time-frequency points wher...

A new update equation for the general multichannel blind deconvolution (MCBD) of a convolved mixture of source signals is derived. It is based on the update equation for blind source separation (BSS), which has been shown to be an alternative interpretation [1] of the natural gradient applied to the minimization of some mutual information criterion [2]. Computational complexity is held at a min...

In Secondary Surveillance Radar (SSR) systems, it is more difficult to locate and recognise aircrafts in the neighbourhood of civil airports since aerial traffic becomes greater. Here, we propose to apply a recent Blind Source Separation (BSS) algorithm based on Time-Frequency Analysis, in order to separate messages sent by different aircrafts and falling in the same radar beam in reception. Th...