We introduce a novel Algorithm for underdetermined convolutive mixture of source signals. Where the convolution is routinely approximated in the short-time Fourier transform (STFT) domain as linear instantaneous mixing in each frequency band. Each source STFT is given a model inspired from nonnegative matrix factorization (NMF) with the -divergence, this divergence is a family of cost functions...

Frequency domain ICA has been used successfully to separate the utterances of interfering speakers in convolutive environments, see e.g. [6],[7]. Improved separation results can be obtained by applying a time frequency mask to the ICA outputs. After using the direction of arrival information for permutation correction, the time frequency mask is obtained with little computational effort. The pr...

Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms. We regard EEG sources as eliciting spatio-temporal activity patterns, corresponding to, e.g. trajectories of activation propagating across cortex. This leads to a...

We address the problem of blind separation of mixtures consisting of pure unknown delays in addition to scalar mixing coefficients. Such a mixture is a hybrid situation resembling both static and convolutive mixtures, but essentially different from both: On one hand, static-mixture approaches cannot be readily applied in this context; On the other hand, conventional convolutive-mixture approach...

Convolutive decorrelation algorithms form a class of powerful algorithms for blind source separation. In contrast to ICA, they are based on vanishing second order cross correlation functions between sources. We provide an analyze an unifying approach for convolu-tive decorrelation procedures. The convolutive decor-relation procedures impose the problem of simultaneously diagonalizing a number o...

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...

This paper presents a novel technique for separating convolutive mixtures of statistically independent non-Gaussian signals without resorting to an a priori knowledge of the sources or the mixing system. This problem is solved in the frequency domain by transforming the convolutive mixture into several instantaneous mixtures which are independently separated using blind source separation (BSS) ...

the reactions of interest occur at the surface of the working electrode. Therefore, we are interested in controlling the potential drop across the interface between the surface of the working electrode and the solution (i.e., the interfacial potential). However, it is impossible to control or measure this interfacial potential without placing another electrode in the solution. Thus, two interfa...

Legal documents data analytics is a very significant process in the field of computational law.Semantically analyzing more challenging since it’s often complicated than open domain documents. Efficient document analysis crucial to current legal applications, such as case-based reasoning, citations, and so on. Due extensive growth data, several statistical machine learning methods have been deve...