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...

This paper presents an extension of the ACDC algorithm introduced by Yeredor for the instantaneous mixing problem to the more general convolutive mixing problem with non-white sources. Further assumptions made on the source signals are their mutual statistical independence, nonstationarity and smoothness of their power spectra. The algorithm iterates the estimation of the mixing system (AC step...

This is a review of analytical methods, such as spectrophotometry, derivative spectrophotometry and various chromatographic (gas chromatography-GC, high-performance liquid chromatography-HPLC, thin-layer chromatography-TLC, high-performance thin-layer chromatography-HPTLC, liquid chromatography-tandem mass spectrometry-LC-MS, microchip electrophoresis-MCE, capillary electrophoresis-CE) and elec...

in this study, simple, fast and reliable cyclic voltammetry (cv), linear sweep voltammetry (lsv), square wave voltammetry (swv) and differential pulse voltammetry (dpv) methods were developed and validated for determination of bosentan in pharmaceutical preparations. the proposed methods were based on electrochemical oxidation of bosentan at platinum electrode in acetonitrile solution containin...

A new method to evaluate the performance of convolutive blind signal separation (BSS) algorithms in acoustic mixtures is presented. The method is able to compute the spectral level enhancement, in a frequency-band based fashion, between the estimated and the residual sources, thus combining two previously defined parameters in time and frequency domain: the signal to interference ratio and the ...

A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors. In many real-world applications these limitations are too restrictive. We propose a method for underdetermined blind source separation of convolutive mixtures. The proposed framewor...

ABSTRACT Using algorithmic complexity to perform blind source separation (BSS) was first proposed by Pajunen. This approach presents the advantage of taking the whole signal structure into account to achieve separation, whereas standard ICA-based methods only use either time-correlations or higher order statistics in order to do so. Another advantage of this approach is that no assumptions abou...

We propose an algorithm for blind separation of sources in convolutive mixtures based on a subspace approach. The advantage of this algorithm is that it reduces a convolutive mixture to an instantaneous mixture by using only second-order statistics (but more sensors than sources). Furthermore, the sources can be separated by using any algorithm for an instantaneous mixture (based generally on f...

In the frequency-domain, a convolutive mixture can be interpreted as several instantaneous mixtures which may be separated using many existing algorithms. The main limitation of this approach is the large number of frequencies that must be considered. In addition, a permutation/amplitude correction must be performed when the sources are recovered in a different order or with different amplitude...

This paper reports the results of a comparative study on blind speech separation (BSS) of two types of convolutive mixtures. The separation criterion is based on Frequency Oriented Principal Components Analysis (FOPCA). This method is compared to two other well-known methods: the Degenerate Unmixing Evaluation Technique (DUET) and Convolutive Fast Independent Component Analysis (C-FICA). The ef...