In this paper, a localization based approach of audio signal separation from binary mixtures is carried out. The audio sources are localized in the spatial domain (azimuth plane) using the delay and amplitude variation cues between two microphones' signals. A coherence based technique is introduced here to localize the audio sources in adverse acoustical environment. The mixture signals are decomposed into a desired number of sub-bands with empirical mode decomposition (EMD) which is a data adaptive filtering scheme suitable for nonlinear and non-stationary signals. Data independent minimum variance beamforming is employed to separate the component sources in underdetermined condition (more sources than sensors). The experimental results of the proposed algorithm show noticeable separation efficiency. It is also found that the sub-band implementation improves the performance compared with and full-band approach