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FlowGrad: Using Motion for Visual Sound Source Localization

2022-11-15 18:12:10
Rajsuryan Singh, Pablo Zinemanas, Xavier Serra, Juan Pablo Bello, Magdalena Fuentes

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2211.08367

PDF

https://arxiv.org/pdf/2211.08367.pdf


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