  In real-world settings, speech signals are almost always affected by
reverberation produced by the working environment; these corrupted signals need
to be \emph{dereverberated} prior to performing, e.g., speech recognition,
speech-to-text conversion, compression, or general audio enhancement. In this
paper, we propose a supervised dereverberation technique using \emph{U-nets
with skip connections}, which are fully-convolutional encoder-decoder networks
with layers arranged in the form of an "U" and connections that "skip" some
layers. Building on this architecture, we address speech dereverberation
through the lens of Late Reverberation Suppression (LS). Via experiments on
synthetic and real-world data with different noise levels and reverberation
settings, we show that our proposed method termed "LS U-net" improves quality,
intelligibility and other performance metrics compared to the original U-net
method and it is on par with the state-of-the-art GAN-based approaches.
