ICA-Based Single Channel Source Separation With Time-Frequency Decomposition

Abstract

This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal. Statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the β distance of Gaussian distribution. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The best separation results were obtained with the use of the s distance of Gaussian distribution, a distance measure based on the knowledge of the probabilistic nature of spectra of original constituent signals of the mixed signal.