  In this paper, we introduce a spectral-domain inverse filtering approach for
single-channel speech de-reverberation using deep convolutional neural network
(CNN). The main goal is to better handle realistic reverberant conditions where
the room impulse response (RIR) filter is longer than the short-time Fourier
transform (STFT) analysis window. To this end, we consider the convolutive
transfer function (CTF) model for the reverberant speech signal. In the
proposed framework, the CNN architecture is trained to directly estimate the
inverse filter of the CTF model. Among various choices for the CNN structure,
we consider the U-net which consists of a fully-convolutional auto-encoder
network with skip-connections. Experimental results show that the proposed
method provides better de-reverberation performance than the prevalent
benchmark algorithms under various reverberation conditions.
