A novel algorithm is proposed in this paper to solve blind source separation of post-nonlinear convolutive mixtures of non-stationary sources. Both convolutive mixing and post-nonlinear distortion are included in the proposed model. Based on the generalized Expectation-Maximization (EM) algorithm, the Maximum Likelihood (ML) approach is developed to estimate the parameters in the model. A set o...

In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important this field as they require few and weak assumptions allow for blindness regarding the original sou...

An adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary signals is proposed. The algorithm is derived by applying natural gradient iterative learning to a novel cost function which is defined according to the wide sense cyclostationarity of signals and can be deemed as a new member of the family of natural gradient algorithms for convolutive m...

Blind Source Separation (BSS) is a statistical approach to separating individual signals from an observed mixture of a group of signals. BSS relies on only very weak assumptions on the signals and the mixing process (hence the “blind” descriptor) and this blindness enables the technique to be used in a wide variety of situations. Research in the field of Blind Source Separation has resulted in ...

We consider the problem of convolutive blind signal separation through the optimization of contrast functions. In this work, we show that some links between contrasts and joint diagonalization criteria can be exhibited in the convolutive case. This allows to devise a constructive algorithm performing MIMO blind equalization, with the help of a joint approximate diagonalization of a set of matri...

In this paper we extend the infomax technique [1] for blind signal separation from the instantaneous mixing case to the convolutive mixing case. Separation in the convolutive case requires an unmixing system which uses present and past values of the observation vector, when the mixing system is causal. Thus, in developing an infomax process, both temporal and spatial dependence of the observati...

A new concept of combining conventional beamforming with independent component analysis (ICA) techniques and its implementation on a multi DSP system is presented. The system consists of two floating point digital signal processors TMS320C6701, an eight channel linear microphone array, an analog/digital converter board and a handheld control unit for stand alone operation. In the two system sta...