UA-Bench: Benchmarking Large Audio-Language Models on Underwater Acoustic Recognition
Large audio-language models (LALMs) are fast becoming a general interface to sound, yet how far their broad pretraining carries into specialized domains remains largely open. Vessel monitoring, maritime safety, and ocean sensing all depend on recognizing underwater signals, but to our knowledge LALMs have not been systematically evaluated on them. Annotated recordings are costly to collect, and a faithful synthetic alternative has to bring together many interacting factors: the structure of detection pulses, communication waveforms, and ship-radiated noise, and the sound-speed profile, geometry, and multipath propagation through which each reaches the receiver. We address this by proposin...
| Authors | Zhe Sun, Liang An, Jiayu Yao et al. |
|---|---|
| Primary category | cs.SD |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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