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Bioacoustic Geolocation: Species Sounds as Geographic Signals

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arxiv 2505.18726 v3 pith:DMNQZZRR submitted 2025-05-24 cs.SD cs.LGeess.AS

Bioacoustic Geolocation: Species Sounds as Geographic Signals

classification cs.SD cs.LGeess.AS
keywords geolocationsignalsspeciesaudiobioacousticgeographicsoundswork
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Can we determine someone's geographic location solely from the sounds they hear? Are acoustic signals enough to localize within a country, state, or even city? In this work, we tackle the challenge of global-scale audio geolocation, with a particular focus on wildlife and natural sounds. We posit that bioacoustic signals contain informative geolocation cues because of well-defined geographic ranges of species. To test this hypothesis, we benchmark image geolocation and soundscape mapping methods, design oracles and species-centric baselines, and propose a hybrid approach that combines species range prediction with retrieval-based geolocation. We further ask whether geolocation improves with species-diverse recordings and spatiotemporal aggregation across neighboring samples. Finally, we extend our study to multimodal geolocation with case studies from movies that combine both audio and visual content. Our results highlight the potential of incorporating bioacoustic signals into geospatial tasks, motivating future work on species recognition and audio geolocation.

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Cited by 2 Pith papers

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    GeoGNN is a two-tower GNN that learns geographic cell embeddings from adjacency graphs and matches them to temporal representations via dot-product similarity plus classification, improving geolocalization accuracy by...

  2. Masked Autoencoders with Limited Data: Does It Work? A Fine-Grained Bioacoustics Case Study

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    In moderate-sized fine-grained bioacoustics, pretraining scale of masked autoencoders on diverse general audio dominates over domain-specific objectives or data curation for transfer performance.