  Speech dereverberation is an important stage in many speech technology
applications. Recent work in this area has been dominated by deep neural
network models. Temporal convolutional networks (TCNs) are deep learning models
that have been proposed for sequence modelling in the task of dereverberating
speech. In this work a weighted multi-dilation depthwise-separable convolution
is proposed to replace standard depthwise-separable convolutions in TCN models.
This proposed convolution enables the TCN to dynamically focus on more or less
local information in its receptive field at each convolutional block in the
network. It is shown that this weighted multi-dilation temporal convolutional
network (WD-TCN) consistently outperforms the TCN across various model
configurations and using the WD-TCN model is a more parameter efficient method
to improve the performance of the model than increasing the number of
convolutional blocks. The best performance improvement over the baseline TCN is
0.55 dB scale-invariant signal-to-distortion ratio (SISDR) and the best
performing WD-TCN model attains 12.26 dB SISDR on the WHAMR dataset.
