Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation

TL;DR

This paper introduces a self-supervised audio-visual learning framework that leverages transformation invariance and equivariance to improve sound source localization and retrieval tasks in videos.

Contribution

It systematically investigates the effects of data augmentations, emphasizing the importance of transformation invariance and equivariance for better multi-modal representations.

Findings

Outperforms previous methods on Flickr-SoundNet and VGG-Sound benchmarks.

Achieves competitive results with supervised methods in audio retrieval.

Enhances generalization in cross-modal retrieval tasks.

Abstract

We present a simple yet effective self-supervised framework for audio-visual representation learning, to localize the sound source in videos. To understand what enables to learn useful representations, we systematically investigate the effects of data augmentations, and reveal that (1) composition of data augmentations plays a critical role, i.e. explicitly encouraging the audio-visual representations to be invariant to various transformations~({\em transformation invariance}); (2) enforcing geometric consistency substantially improves the quality of learned representations, i.e. the detected sound source should follow the same transformation applied on input video frames~({\em transformation equivariance}). Extensive experiments demonstrate that our model significantly outperforms previous methods on two sound localization benchmarks, namely, Flickr-SoundNet and VGG-Sound. Additionally, we also evaluate audio retrieval and cross-modal retrieval tasks. In both cases, our self-supervised models demonstrate superior retrieval performances, even competitive with the supervised approach in audio retrieval. This reveals the proposed framework learns strong multi-modal representations that are beneficial to sound localisation and generalization to further applications. \textit{All codes will be available}.