  From whirling ceiling fans to ticking clocks, the sounds that we hear subtly
vary as we move through a scene. We ask whether these ambient sounds convey
information about 3D scene structure and, if so, whether they provide a useful
learning signal for multimodal models. To study this, we collect a dataset of
paired audio and RGB-D recordings from a variety of quiet indoor scenes. We
then train models that estimate the distance to nearby walls, given only audio
as input. We also use these recordings to learn multimodal representations
through self-supervision, by training a network to associate images with their
corresponding sounds. These results suggest that ambient sound conveys a
surprising amount of information about scene structure, and that it is a useful
signal for learning multimodal features.
