  Deep learning based single-channel speech enhancement tries to train a neural
network model for the prediction of clean speech signal. There are a variety of
popular network structures for single-channel speech enhancement, such as TCNN,
UNet, WaveNet, etc. However, these structures usually contain millions of
parameters, which is an obstacle for mobile applications. In this work, we
proposed a light weight neural network for speech enhancement named TFCN. It is
a temporal-frequential convolutional network constructed of dilated
convolutions and depth-separable convolutions. We evaluate the performance of
TFCN in terms of Short-Time Objective Intelligibility (STOI), perceptual
evaluation of speech quality (PESQ) and a series of composite metrics named
Csig, Cbak and Covl. Experimental results show that compared with TCN and
several other state-of-the-art algorithms, the proposed structure achieves a
comparable performance with only 93,000 parameters. Further improvement can be
achieved at the cost of more parameters, by introducing dense connections and
depth-separable convolutions with normal ones. Experiments also show that the
proposed structure can work well both in causal and non-causal situations.
