  Single-channel speech dereverberation aims at extracting a dry speech signal
from a recording affected by the acoustic reflections in a room. However, most
current deep learning-based approaches for speech dereverberation are not
interpretable for room acoustics, and can be considered as black-box systems in
that regard. In this work, we address this problem by regularizing the training
loss using a novel physical coherence loss which encourages the room impulse
response (RIR) induced by the dereverberated output of the model to match the
acoustic properties of the room in which the signal was recorded. Our
investigation demonstrates the preservation of the original dereverberated
signal alongside the provision of a more physically coherent RIR.
