The Sonar Moment: Benchmarking Audio-Language Models in Audio Geo-Localization
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The introduction of AGL1K marks a significant advancement in audio geo-localization, providing a benchmark with 1,444 curated audio clips across 72 countries. By employing the Audio Localizability metric, researchers have enhanced the quality of recordings for evaluation. Results indicate that closed-source audio language models outperform open-source counterparts, with linguistic cues playing a key role in predictions. This benchmark could improve geospatial reasoning in ALMs, addressing previous limitations in audio-based localization.
The Launch of AGL1K: A New Benchmark for Audio Geo-Localization
A new benchmark for audio geo-localization, AGL1K, has been introduced for audio language models (ALMs). It includes data from 72 countries and territories, addressing a gap in quality audio-location pairs.
The AGL1K dataset features 1,444 curated audio clips sourced through a crowd-sourced platform. An innovative Audio Localizability metric was implemented to evaluate the informativeness of each audio sample.
Key Findings from Evaluations
Initial evaluations of AGL1K on 16 different ALMs showed notable advancements in audio geo-localization. Closed-source models outperformed open-source models, indicating potential advantages of proprietary solutions.
Evaluations highlighted that linguistic clues significantly influenced the models' predictive accuracy, suggesting that language usage in audio samples is critical for effective geo-localization.
Regional Bias and Error Analysis
The research identified patterns of regional bias and common error sources in ALMs, providing insights for future improvements in model design.
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📰 Original Source: https://arxiv.org/abs/2601.03227v1
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