Independent Vector Analysis using Non-Spherical Joint Densities for the Separation of Speech Signals

Abstract

We propose a new blind source separation approach that models the inherent signal dependencies such as those observed in speech signals in order to solve the problem of separating convolved sources. The frequency domain methods for the convolved mixture problem require a solution to the wellknown permutation problem. Our approach is based on assuming a vector representation of the source signal where its multidimensional joint densities are non-spherical. Spherical distributions may be adequate for signals that exhibit uniform dependencies across frequencies but in case of speech signals we can observe stronger dependencies for neighboring frequency bins and almost no dependency for frequency bins that are far apart. The non-spherical joint density model takes into account this phenomenon. For the separation of convolved sources, the proposed method demonstrates consistent performance over previous methods and improved performance over the spherical joint density representations.