  We learn rich natural sound representations by capitalizing on large amounts
of unlabeled sound data collected in the wild. We leverage the natural
synchronization between vision and sound to learn an acoustic representation
using two-million unlabeled videos. Unlabeled video has the advantage that it
can be economically acquired at massive scales, yet contains useful signals
about natural sound. We propose a student-teacher training procedure which
transfers discriminative visual knowledge from well established visual
recognition models into the sound modality using unlabeled video as a bridge.
Our sound representation yields significant performance improvements over the
state-of-the-art results on standard benchmarks for acoustic scene/object
classification. Visualizations suggest some high-level semantics automatically
emerge in the sound network, even though it is trained without ground truth
labels.
