  Speech dereverberation is often an important requirement in robust speech
processing tasks. Supervised deep learning (DL) models give state-of-the-art
performance for single-channel speech dereverberation. Temporal convolutional
networks (TCNs) are commonly used for sequence modelling in speech enhancement
tasks. A feature of TCNs is that they have a receptive field (RF) dependent on
the specific model configuration which determines the number of input frames
that can be observed to produce an individual output frame. It has been shown
that TCNs are capable of performing dereverberation of simulated speech data,
however a thorough analysis, especially with focus on the RF is yet lacking in
the literature. This paper analyses dereverberation performance depending on
the model size and the RF of TCNs. Experiments using the WHAMR corpus which is
extended to include room impulse responses (RIRs) with larger T60 values
demonstrate that a larger RF can have significant improvement in performance
when training smaller TCN models. It is also demonstrated that TCNs benefit
from a wider RF when dereverberating RIRs with larger RT60 values.
