Toward Interactive Sound Source Localization: Better Align Sight and Sound!

Recent studies on learning-based sound source localization have primarily focused on localization performance. However, prior work and existing benchmarks often overlook a crucial aspect: cross-modal interaction, which is essential for interactive sound source localization. This interaction is vital for understanding semantically matched or mismatched audio-visual events, such as silent objects or true sound sources among multiple objects. In this work, we comprehensively examine the cross-modal interaction of existing methods, benchmarks, evaluation metrics, and cross-modal understanding tasks. We identify the overlooked points of previous studies and make several contributions to address them. First, we propose a learning framework that incorporates retrieval-based and hand-crafted augmentation techniques, enhancing cross-modal interaction through cross-modal alignment. Second, we introduce new evaluation metrics to accurately and rigorously assess localization methods, focusing on both localization performance and cross-modal interaction. Third, to thoroughly analyze interactive sound source localization, we present a new semi-synthetic benchmark with diverse categorical combinations. Finally, we evaluate both interactive sound source localization and auxiliary cross-modal retrieval tasks, benchmarking competing methods alongside our own. Our new benchmark and evaluation metrics reveal that previous methods struggle with interactive sound source localization tasks, largely due to their limited cross-modal interaction capabilities. Our method, which features enhanced cross-modal alignment, demonstrates superior sound source localization and cross-modal interaction 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 both new and standard evaluation metrics.