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In this article a new neural network for speech enhancement is proposed where single-channel noisy speech is processed in order to improve its intelligibility and quality. It is based on the U-net architecture, i.e. it is composed of two main blocks: encoder and decoder. Some of the corresponding layers in the encoder and decoder are connected with skip connections. In most of the encoder-decoder neural networks for speech enhancement known from the literature, the time-frequency resolution of the hidden feature maps is reduced. The main strategy in the presented approach is to maintain the time-frequency resolution of feature maps at all levels of the network while having large receptive field at the same time. In order to obtain features dependent on wide context we propose neural network units based on recurrent cells or dilated convolutions. The proposed neural network was evaluated using WSJ0 and …