FlowGrad: Using Motion for Visual Sound Source Localization

Most recent work in visual sound source localization relies on semantic audio-visual representations learned in a self-supervised manner, and by design excludes temporal information present in videos. While it proves to be effective for widely used benchmark datasets, the method falls short for chal...

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Bibliographic Details
Published in:arXiv.org 2023-04
Main Authors: Singh, Rajsuryan, Zinemanas, Pablo, Serra, Xavier, Juan Pablo Bello, Fuentes, Magdalena
Format: Article
Language:English
Subjects:
Optical flow (image analysis)
Scene analysis
Sound localization
Sound sources
Online Access:Get full text
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Summary:Most recent work in visual sound source localization relies on semantic audio-visual representations learned in a self-supervised manner, and by design excludes temporal information present in videos. While it proves to be effective for widely used benchmark datasets, the method falls short for challenging scenarios like urban traffic. This work introduces temporal context into the state-of-the-art methods for sound source localization in urban scenes using optical flow as a means to encode motion information. An analysis of the strengths and weaknesses of our methods helps us better understand the problem of visual sound source localization and sheds light on open challenges for audio-visual scene understanding.
ISSN:2331-8422