Learning from Silence and Noise for Visual Sound Source Localization


Xavier Juanola (Universitat Pompeu Fabra), Giovana Morais (New York University), Magdalena Fuentes (New York University), Gloria Haro (Universitat Pompeu Fabra)
The 35th British Machine Vision Conference
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Abstract

Visual sound source localization is a fundamental perception task that aims to detect the location of sounding sources in a video given its audio. Despite recent progress, we identify two shortcomings in current methods: 1) most approaches perform poorly in cases with low audio-visual semantic correspondence such as silence, noise, and offscreen sounds, i.e. in the presence of negative audio; and 2) most prior evaluations are limited to positive cases, where both datasets and metrics convey scenarios with a single visible sound source in the scene. To address this, we introduce three key contributions. First, we propose a new training strategy that incorporates silence and noise, which improves performance in positive cases, while being more robust against negative sounds. Our resulting self-supervised model, SSL-SaN, achieves state-of-the-art performance compared to other self-supervised models, both in sound localization and cross-modal retrieval. Second, we propose a new metric that quantifies the trade-off between alignment and separability of auditory and visual features across positive and negative audio-visual pairs. Third, we present IS3+, an extended and improved version of the IS3 synthetic dataset with negative audio. Our data, metrics and code are available on the \href{https://xavijuanola.github.io/SSL-SaN/}{\color{black}{Project page}}.

Citation

@inproceedings{Juanola_2025_BMVC,
author    = {Xavier Juanola and Giovana Morais and Magdalena Fuentes and Gloria Haro},
title     = {Learning from Silence and Noise for Visual Sound Source Localization},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_364/paper.pdf}
}


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