Abstract: A blind speech source separation method for the underdetermined convolution is proposed via combining Fast Independent Component Analysis（FastICA） and adaptive nonlinear binary time-frequency masking.By estimating nonlinear binary masks from the outputs of a FastICA algorithm，it is possible in an iterative way to extract basic speech signals from a convolutive mixture.The basic signals are afterwards improved by the masks merging.The stereo property of the extracted speech signals can be maintained.The simulation results demonstrate that the proposed separation method outperforms DUET and BLUES methods.The signal-noise-ratio gain of the results is greatly improved.