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We propose a novel, end-to-end model based on convolutional and recurrent neural networks for speech enhancement. Our model is purely data-driven: it does not make any assumptions about the type or the stationarity of the noise. In contrast to existing methods that use multilayer perceptrons (MLPs), we employ both convolutional and recurrent neural network architectures. Thus, our approach allows us to exploit local structures in both the spatial and temporal domains. By incorporating priordoi:10.1109/icassp.2018.8462155 dblp:conf/icassp/ZhaoZTL18 fatcat:afhikwhftbbypbkebajlj7mofy