Our everyday auditory experience is shaped by the acoustics of the indoor
environments in which we live. Room acoustics modeling is aimed at establishing
mathematical representations of acoustic wave propagation in such environments.
These representations are relevant to a variety of problems ranging from
echo-aided auditory indoor navigation to restoring speech understanding in
cocktail party scenarios. Many disciplines in science and engineering have
recently witnessed a paradigm shift powered by deep learning (DL), and room
acoustics research is no exception. The majority of deep, data-driven room
acoustics models are inspired by DL-based speech and image processing, and
hence lack the intrinsic space-time structure of acoustic wave propagation.
More recently, DL-based models for room acoustics that include either geometric
or wave-based information have delivered promising results, primarily for the
problem of sound field reconstruction. In this review paper, we will provide an
extensive and structured literature review on deep, data-driven modeling in
room acoustics. Moreover, we position these models in a framework that allows
for a conceptual comparison with traditional physical and data-driven models.
Finally, we identify strengths and shortcomings of deep, data-driven room
acoustics models and outline the main challenges for further research.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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