Underdetermined Source Separation in the Time-Frequency Domain

Abstract

Underdetermined blind source separation (UBSS) is a challenging problem that has recently been formulated in the time-frequency domain. Previous work in the area of UBSS problem focuses on using sparse representations of signals, such as matching pursuit and wavelet packet decomposition, for identifying the sources. However, these methods are in general computationally expensive and rely on the choice of an appropriate basis function for obtaining a sparse representation. In this paper, we propose a new approach based on Cohen's class of distributions. The new approach takes advantage of the high resolution of time-frequency distributions for obtaining a sparse representation, and separates the sources by a simple clustering algorithm followed by a convex optimization problem. Compared to other time-frequency based separation methods, the presented approach is characterized by its simplicity and ease of implementation. Experimental results indicate the effectiveness of the proposed approach at separating the sparse signals in the time-frequency domain.