Examination of convolutive blind source separation algorithms based on information theoretic criterion and second-order statistics for cell-phone application

In this paper, we have investigated the performance of a blind source separation (BSS) method based on second order statistics (SOS) and an information theoretic BSS approach based on Kullback-Leibler (KL) divergence criterion for the data of cell-phone application. For this purpose, a hybrid time-frequency approach and two different SOS and KL-based algorithms within this framework are exploited. The performance of these algorithms is examined using the real-world data recorded for evaluating BSS algorithms for cell-phone application. Simulation results reveal that both algorithms have nearly the same performance after convergence. However, due to the simplicity and slightly better convergence properties, the SOS-based algorithm is preferred for practical purposes.