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In this paper, we propose an iterative deep neural network (DNN)-based binaural source separation scheme, for recovering two concurrent speech signals in a room environment. Besides the commonly-used spectral features, the DNN also takes non-linearly wrapped binaural spatial features as input, which are refined iteratively using parameters estimated from the DNN output via a feedback loop. Different DNN structures have been tested, including a classic multilayer perception regressiondoi:10.1109/icassp.2018.8462603 dblp:conf/icassp/Liu0JWC18 fatcat:gjlmw2uwajbq3g4o2wre5fsedi