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

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 beta 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 beta 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.