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.

通过使用大量的野外未标记的声音数据，我们利用视觉和声音之间的自然同步，使用两百万个未标记的视频学习声学表示，提出了一种学生 - 教师训练过程，将视觉知识转移到声音模态中，为声音场景 / 对象分类的标准基准提供了显着的性能提升，即使没有地面真实标签，声音网络自动形成一些高级语义。