Papers › Ambient Sound Provides Supervision for Visual Learning

Ambient Sound Provides Supervision for Visual Learning

25 Aug 2016arXiv:1608.07017archive 2025-07-28

Andrew Owens, Jiajun Wu, Josh H. McDermott, William T. Freeman, Antonio Torralba

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.

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