  We introduce PixelPlayer, a system that, by leveraging large amounts of
unlabeled videos, learns to locate image regions which produce sounds and
separate the input sounds into a set of components that represents the sound
from each pixel. Our approach capitalizes on the natural synchronization of the
visual and audio modalities to learn models that jointly parse sounds and
images, without requiring additional manual supervision. Experimental results
on a newly collected MUSIC dataset show that our proposed Mix-and-Separate
framework outperforms several baselines on source separation. Qualitative
results suggest our model learns to ground sounds in vision, enabling
applications such as independently adjusting the volume of sound sources.
