  When designing fully-convolutional neural network, there is a trade-off
between receptive field size, number of parameters and spatial resolution of
features in deeper layers of the network. In this work we present a novel
network design based on combination of many convolutional and recurrent layers
that solves these dilemmas. We compare our solution with U-nets based models
known from the literature and other baseline models on speech enhancement task.
We test our solution on TIMIT speech utterances combined with noise segments
extracted from NOISEX-92 database and show clear advantage of proposed solution
in terms of SDR (signal-to-distortion ratio), SIR (signal-to-interference
ratio) and STOI (spectro-temporal objective intelligibility) metrics compared
to the current state-of-the-art.
