Blind separation of instantaneous mixtures of nonstationary sources

Most source separation algorithms are based on a model of stationary source s. However, it is a simple matter to take advantage of possible nonstationa rities of the sources to achieve separation. This paper develops novel appr oaches in this direction based on the principles of maximum likelihood and minimum mutual information. These principles are exploited by efficient alg orithms in both the off-line case (via a new joint diagonalization procedur e) and in the on-line case (via a Newton-like procedure). Some experiments showing the good performance of our algorithms and evidencing an interestin g feature of our methods are presented: their ability to achieve a kind of super-efficiency. The paper concludes with a discussion contrasting separat ing methods for non-Gaussian and nonstationary models and emphasizing that, as a matter of fact, "what makes the algorithms work" is-strictly speaking -not the nonstationarity itself but rather the property that each realizati on of the source signals has a time-varying envelope.