A Unifying View on Blind Source Separation of Convolutive Mixtures Based on Independent Component Analysis

Blind Source Separation for Convolutive Mixtures Based on Complexity Minimization

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...

Convolutive Blind Source Separation for Noisy Mixtures

The problem of separating convolutive mixtures of unknown time series arises in several application domains, a prominent example being the so-called cocktail party problem, where we want to recover the speech signals of multiple speakers who are simultaneously talking in a room. The room may be reverberant due to reflections on the walls, i.e., the original source signals sq(n), q = 1, . . . , ...

Blind source separation for convolutive mixtures

This paper introduces the blind source separation (BSS) of convolutive mixtures of acoustic signals, especially speech. A statistical and computational technique, called independent component analysis (ICA), is examined. By achieving nonlinear decorrelation, nonstationary decorrelation, or time-delayed decorrelation, we can find source signals only from observed mixed signals. Particular attent...

Independent component analysis and blind source separation

Independent Component Analysis and Blind Source Separation