The Sonar Moment: Benchmarking Audio-Language Models in Audio Geo-Localization
Geo-localization aims to infer the geographic origin of a given signal. In computer vision, geo-localization has served as a demanding benchmark for compositional reasoning and is relevant to public safety. In contrast, progress on audio geo-localization has been constrained by the lack of high-quality audio-location pairs. To address this gap, we introduce AGL1K, the first audio geo-localization benchmark for audio language models (ALMs), spanning 72 countries and territories. To extract reliably localizable samples from a crowd-sourced platform, we propose the Audio Localizability metric that quantifies the informativeness of each recording, yielding 1,444 curated audio clips. Evaluations on 16 ALMs show that ALMs have emerged with audio geo-localization capability. We find that closed-source models substantially outperform open-source models, and that linguistic clues often dominate as a scaffold for prediction. We further analyze ALMs' reasoning traces, regional bias, error causes, and the interpretability of the localizability metric. Overall, AGL1K establishes a benchmark for audio geo-localization and may advance ALMs with better geospatial reasoning capability.
695df23bc03d6d81e4399fec
Ruixing Zhang
claimed_verified
2026-01-07T09:25:30.396Z
66b9bfade085a5c7e7c24669
/avatars/db9a5ca6d5568555a97c578ba0820508.svg
Rising0321
RisingZhang
user
RisingZhang
695df23bc03d6d81e4399fed
Zihan Liu
695df23bc03d6d81e4399fee
Leilei Sun
695df23bc03d6d81e4399fef
Tongyu Zhu
695df23bc03d6d81e4399ff0
Weifeng Lv
The Sonar Moment: Benchmarking Audio-Language Models in Audio Geo-Localization
Geo-localization aims to infer the geographic origin of a given signal. In computer vision, geo-localization has served as a demanding benchmark for compositional reasoning and is relevant to public safety. In contrast, progress on audio geo-localization has been constrained by the lack of high-quality audio-location pairs. To address this gap, we introduce AGL1K, the first audio geo-localization benchmark for audio language models (ALMs), spanning 72 countries and territories. To extract reliably localizable samples from a crowd-sourced platform, we propose the Audio Localizability metric that quantifies the informativeness of each recording, yielding 1,444 curated audio clips. Evaluations on 16 ALMs show that ALMs have emerged with audio geo-localization capability. We find that closed-source models substantially outperform open-source models, and that linguistic clues often dominate as a scaffold for prediction. We further analyze ALMs' reasoning traces, regional bias, error causes, and the interpretability of the localizability metric. Overall, AGL1K establishes a benchmark for audio geo-localization and may advance ALMs with better geospatial reasoning capability.
Audio geo-localization benchmark AGL1K is introduced to advance audio language models' geospatial reasoning capabilities through curated audio clips and evaluation across multiple models.
audio geo-localization audio language models AGL1K audio localizability metric compositional reasoning public safety crowd-sourced platform geospatial reasoning