  The sound of crashing waves, the roar of fast-moving cars -- sound conveys
important information about the objects in our surroundings. In this work, we
show that ambient sounds can be used as a supervisory signal for learning
visual models. To demonstrate this, we train a convolutional neural network to
predict a statistical summary of the sound associated with a video frame. We
show that, through this process, the network learns a representation that
conveys information about objects and scenes. We evaluate this representation
on several recognition tasks, finding that its performance is comparable to
that of other state-of-the-art unsupervised learning methods. Finally, we show
through visualizations that the network learns units that are selective to
objects that are often associated with characteristic sounds. This paper
extends an earlier conference paper, Owens et al. 2016, with additional
experiments and discussion.
