TraSNav: Transient-Sound-Aware Audio-Visual Navigation

Audio-visual navigation has received considerable attention in recent years. However, in realistic multi-source environments, target sounds may occur as brief and temporally sparse acoustic events, providing intermittent and limited evidence for navigation. The key challenge is that these transient target cues can be easily affected by competing sounds, while their short-lived information must also be integrated with navigation context accumulated over much longer time scales. To address this problem, we propose TraSNav, a navigation framework for transient target sounds. Our key idea is to maintain an event-level memory that accumulates and consolidates information from fragmented acoustic observations to improve robustness against competing sounds, and then adaptively fuse this event memory with long-term navigation memory to handle temporal-scale differences. Experimental results demonstrate that TraSNav consistently outperforms representative audio-visual navigation methods, achieving up to a 26.41% relative improvement in success rate when each target-sound event is shortened to 10% of its original duration in multi-source environments.