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The Sound of Motions

Author(s)
Zhao, Hang; Gan, Chuang; Ma, Wei-Chiu; Torralba, Antonio
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Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
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Abstract
© 2019 IEEE. Sounds originate from object motions and vibrations of surrounding air. Inspired by the fact that humans is capable of interpreting sound sources from how objects move visually, we propose a novel system that explicitly captures such motion cues for the task of sound localization and separation. Our system is composed of an end-to-end learnable model called Deep Dense Trajectory (DDT), and a curriculum learning scheme. It exploits the inherent coherence of audio-visual signals from a large quantities of unlabeled videos. Quantitative and qualitative evaluations show that comparing to previous models that rely on visual appearance cues, our motion based system improves performance in separating musical instrument sounds. Furthermore, it separates sound components from duets of the same category of instruments, a challenging problem that has not been addressed before.
Date issued
2019-10
URI
https://hdl.handle.net/1721.1/137169
Department
MIT-IBM Watson AI Lab; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the IEEE International Conference on Computer Vision
Publisher
IEEE
Citation
Zhao, Hang, Gan, Chuang, Ma, Wei-Chiu and Torralba, Antonio. 2019. "The Sound of Motions." Proceedings of the IEEE International Conference on Computer Vision, 2019-October.
Version: Original manuscript

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