  The goal of this work is to localize sound sources in visual scenes with a
self-supervised approach. Contrastive learning in the context of sound source
localization leverages the natural correspondence between audio and visual
signals where the audio-visual pairs from the same source are assumed as
positive, while randomly selected pairs are negatives. However, this approach
brings in noisy correspondences; for example, positive audio and visual pair
signals that may be unrelated to each other, or negative pairs that may contain
semantically similar samples to the positive one. Our key contribution in this
work is to show that using a less strict decision boundary in contrastive
learning can alleviate the effect of noisy correspondences in sound source
localization. We propose a simple yet effective approach by slightly modifying
the contrastive loss with a negative margin. Extensive experimental results
show that our approach gives on-par or better performance than the
state-of-the-art methods. Furthermore, we demonstrate that the introduction of
a negative margin to existing methods results in a consistent improvement in
performance.
