Conventional Blind Source Separation (BSS) algorithms separate the sources assuming the number of sources equals to that of observations. BSS algorithms have been developed based on an assumption that all sources have non-Gaussian distributions. Most of the instances, these algorithms separate speech signals with super-Gaussian distributions. However, in real world examples there exist speech s...

This paper presents a robust and precise method for solving the permutation problem of frequency-domain blind source separation. It is based on two previous approaches: the direction of arrival estimation approach and the inter-frequency correlation approach. We discuss the advantages and disadvantages of the two approaches, and integrate them to exploit the both advantages. We also present a c...

We present and test an extension of slow feature analysis as a novel approach to nonlinear blind source separation. The algorithm relies on temporal correlations and iteratively reconstructs a set of statistically independent sources from arbitrary nonlinear instantaneous mixtures. Simulations show that it is able to invert a complicated nonlinear mixture of two audio signals with a high reliab...

In this paper, a prototype of novel algorithm for blind separation of convolutive mixtures of audio sources is proposed. The method works in time-domain, and it is based on the recently very successful algorithm EFICA for Independent Component Analysis, which is an enhanced version of more famous FastICA. Performance of the new algorithm is very promising, at least, comparable to other (mostly ...

While Blind Source Separation (BSS) for linear mixtures has been well studied, the problem for nonlinear mixtures is still thought not to have a general solution. Each of the techniques proposed for solving BSS in nonlinear mixtures works mainly on specific models and cannot be generalized for many other realistic applications. Our approach in this paper is quite different and targets the gener...

This communication deals with the problem of blind separation of an instantaneous linear mixture of mutually un-correlated sources. A second order source separation technique exploiting the time coherence of the source signals is considered. Asymptotic performance analysis of the proposed method is performed. Several numerical simulations are presented to demonstrate the eeectiveness of the pro...

Renyi’s entropy can be used as a cost function for blind source separation (BSS). Previous works have emphasized the advantage of setting Renyi’s exponent to a value different from one in the context of BSS. In this paper, we focus on zero-order Renyi’s entropy minimization for the blind extraction of bounded sources (BEBS). We point out the advantage of choosing the extended zero-order Renyi’s...

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...

The aim of this study was to demonstrate the change in separation-individuation between late adolescents in the first, second, third and fourth year of higher education. The study sample used for this longitudinal study consisted of 148 students attending class studies, computer education and science education departments of Kırıkkale University. The Separation-Individuation Test of Adolescence...

In the basic signal model of blind source separation (BSS), an unknown linear mixing process is assumed. While this ensures under mild conditions a suuciently unique solution, it is desirable to extend the problem to nonlinear mixtures. Unfortunately the nonlinear case is much more diicult to handle, and brings serious indeterminacies to the solutions in the general case. In this paper we propo...