Aligning Sight and Sound: Advanced Sound Source Localization Through Audio-Visual Alignment

TL;DR

This paper introduces a new synthetic benchmark, evaluation metrics, and a cross-modal alignment learning framework to improve and thoroughly assess interactive sound source localization and cross-modal understanding.

Contribution

It presents a novel approach with enhanced cross-modal alignment, new benchmarks, and metrics, addressing limitations of previous studies in sound source localization.

Findings

The proposed method outperforms existing approaches in localization accuracy.

New benchmarks reveal overlooked issues in current sound source localization methods.

Enhanced evaluation metrics provide a more comprehensive assessment of cross-modal interaction.

Abstract

Recent studies on learning-based sound source localization have mainly focused on the localization performance perspective. However, prior work and existing benchmarks overlook a crucial aspect: cross-modal interaction, which is essential for interactive sound source localization. Cross-modal interaction is vital for understanding semantically matched or mismatched audio-visual events, such as silent objects or off-screen sounds. In this paper, we first comprehensively examine the cross-modal interaction of existing methods, benchmarks, evaluation metrics, and cross-modal understanding tasks. Then, we identify the limitations of previous studies and make several contributions to overcome the limitations. First, we introduce a new synthetic benchmark for interactive sound source localization. Second, we introduce new evaluation metrics to rigorously assess sound source localization methods, focusing on accurately evaluating both localization performance and cross-modal interaction ability. Third, we propose a learning framework with a cross-modal alignment strategy to enhance cross-modal interaction. Lastly, we evaluate both interactive sound source localization and auxiliary cross-modal retrieval tasks together to thoroughly assess cross-modal interaction capabilities and benchmark competing methods. Our new benchmarks and evaluation metrics reveal previously overlooked issues in sound source localization studies. Our proposed novel method, with enhanced cross-modal alignment, shows superior sound source localization performance. This work provides the most comprehensive analysis of sound source localization to date, with extensive validation of competing methods on both existing and new benchmarks using new and standard evaluation metrics.