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arXiv · 2010.05466

Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching

Di Hu·Rui Qian·Minyue Jiang·Xiao Tan·Shilei Wen·Errui Ding·Weiyao Lin·Dejing Dou
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Abstract

Discriminatively localizing sounding objects in cocktail-party, i.e., mixed sound scenes, is commonplace for humans, but still challenging for machines. In this paper, we propose a two-stage learning framework to perform self-supervised class-aware sounding object localization. First, we propose to learn robust object representations by aggregating the candidate sound localization results in the single source scenes. Then, class-aware object localization maps are generated in the cocktail-party scenarios by referring the pre-learned object knowledge, and the sounding objects are accordingly selected by matching audio and visual object category distributions, where the audiovisual consistency is viewed as the self-supervised signal. Experimental results in both realistic and synthesized cocktail-party videos demonstrate that our model is superior in filtering out silent objects and pointing out the location of sounding objects of different classes. Code is available at https://github.com/DTaoo/Discriminative-Sounding-Objects-Localization.

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Di Hu, Rui Qian, Minyue Jiang, Xiao Tan, Shilei Wen, Errui Ding, Weiyao Lin, Dejing Dou. 2020-10-12. Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching. https://arxiv.org/abs/2010.05466

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