@proceedings {2618,
	title = {Lecture Notes in Computer ScienceComputer Vision {\textendash} ECCV 2016Ambient Sound Provides Supervision for Visual Learning},
	year = {2016},
	month = {10/2016},
	pages = {801 - 816},
	address = {Cham},
	abstract = {<p>The sound of crashing waves, the roar of fast-moving cars {\textendash} 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.</p>
},
	keywords = {convolutional networks, Sound, unsupervised learning},
	isbn = {978-3-319-46447-3},
	issn = {0302-9743},
	doi = {10.1007/978-3-319-46448-010.1007/978-3-319-46448-0_48},
	url = {http://link.springer.com/10.1007/978-3-319-46448-0},
	author = {Owens, Andrew and Isola, P. and Josh H. McDermott and William T. Freeman and Torralba, Antonio}
}
