Which One Is Sounding? Binaural Sounding Instance Disambiguation for Audio-Visual Segmentation

Authors: Haoze Ma , Yidi Li , Yu Ma , Pengyuan Zhao , Yi Shi , Bin Ren , Yanwen Guo , Dimitrios Kanoulas

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Abstract: Existing audio-visual segmentation methods mainly predict sound-related regions or category masks and often fail to identify the sounding instance when multiple same-class candidates coexist. Recent audio-visual instance segmentation methods address instance-level prediction but rely largely on motion and cross-frame association, which may become unreliable under sparse observations or inconspicuous motion. To this end, we introduce binaural audio, whose interaural differences encode source-direction information, to provide spatial constraints for distinguishing the sounding instance among same-class visual candidates. We propose Binaural Sounding Instance Disambiguation for Audio-Visual Segmentation (BiSID-AVS), which progressively refines category-level candidate responses into a mask of the true sounding instance. BiSID-AVS first grounds category-consistent candidate regions through audio prompting, then projects binaural cues into an image-plane spatial response map, and finally refines the true sounding instance through dual-level spatial conditioning at the feature and decoder levels. Experiments on the binaurally extended AVS-Synthetic benchmark and Habitat-AVS dataset for robotic indoor perception show that BiSID-AVS achieves the best performance on the multi-candidate subsets, improving instance-level mIoU by 35.19 and 41.21 percentage points, respectively, while substantially reducing false activation on non-sounding same-class regions.

Comments:

12 pages, 5 figures, including appendices

Subjects:

Artificial Intelligence (cs.AI) ; Sound (cs.SD)

Cite as:

arXiv:2611.06074 [cs.AI]

(or

arXiv:2611.06074v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.06074

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Haoze Ma [ view email ]

[v1]

Wed, 4 Nov 2026 07:18:41 UTC (3,701 KB)

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