This article deals with the problem of blind source separation in the case of a linear and instantaneous mixture. We first investigate the behavior of known independent component analysis (ICA) methods in the case where the independence assumption is violated: specific dependent sources are introduced and it is shown that, depending on the source vector, the separation may be successful or not....

Nonnegative tensor factorization (NTF) of multichannel spectrograms under PARAFAC structure has recently been proposed by Fitzgerald et al as a mean of performing blind source separation (BSS) of multichannel audio data. In this paper we investigate the statistical source models implied by this approach. We show that it implicitly assumes a nonpoint-source model contrasting with usual BSS assum...

This paper concerns the blind separation of P complex convolutive mixtures of N statistically independent complex sources, with underdetermined or noisy mixtures i.e. P < N . Our approach exploits the assumed distinct statistical properties of the sources: P sources are non-stationary, while the others are stationary. Our method achieves the ”partial separation” of the P non-stationary sources....

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...