Go to ACL ARR 2026 January homepageAGL1K: An Audio Geo-Localization Benchmark for Audio-Language Models
Keywords: Audio Geo-Localization, Audio Language Model, Benchmark
Abstract: 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.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: benchmarking, evaluation
Contribution Types: Data resources
Languages Studied: English, Chinese, French, German, Swedish, Turkish, etc.
Submission Number: 764
Loading