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For the nonlinear acoustic echo cancellation, we present an adaptive learning of the saturation effect of the amplifier and the room propagation in terms of the hard-clipping and the FIR system. The conventional learning algorithms are based on a gradient descent method, i.e., rely on local information, which results in a major drawback that the estimation of the hard-clipping is trapped in local minima. In this paper, we solve this drawback by exploiting global information embodied as a setdoi:10.1109/icassp.2014.6853809 dblp:conf/icassp/KurodaYY14 fatcat:dm6if5x3qjekppkfoln4ug24nq