Bioacoustic Geolocation: Species Sounds as Geographic Signals

Mustafa Chasmai, Wuao Liu, Subhransu Maji, Grant Van Horn
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:13089-13114, 2026.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-chasmai26b, title = {Bioacoustic Geolocation: Species Sounds as Geographic Signals}, author = {Chasmai, Mustafa and Liu, Wuao and Maji, Subhransu and Horn, Grant Van}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {13089--13114}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/chasmai26b/chasmai26b.pdf}, url = {https://proceedings.mlr.press/v306/chasmai26b.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Bioacoustic Geolocation: Species Sounds as Geographic Signals %A Mustafa Chasmai %A Wuao Liu %A Subhransu Maji %A Grant Van Horn %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-chasmai26b %I PMLR %P 13089--13114 %U https://proceedings.mlr.press/v306/chasmai26b.html %V 306 %X 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.
APA
Chasmai, M., Liu, W., Maji, S. & Horn, G.V.. (2026). Bioacoustic Geolocation: Species Sounds as Geographic Signals. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:13089-13114 Available from https://proceedings.mlr.press/v306/chasmai26b.html.

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