# Bioacoustic Geolocation: Species Sounds as Geographic Signals

arXiv: https://arxiv.org/abs/2505.18726 · HTML: https://mlsanity.com/paper/2505.18726/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2025.

## Built on by (most cited first, 4 of 4)

- [MetaPerch: Learning from metadata for bioacoustics foundation models](https://mlsanity.com/paper/2607.14072/) (2026)
- [Interpretable Perception and Reasoning for Audiovisual Geolocation](https://mlsanity.com/paper/2603.05708/) (2026)
- [Masked Autoencoders with Limited Data: Does It Work? A Fine-Grained Bioacoustics Case Study](https://mlsanity.com/paper/2605.14031/) (2026)
- [GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks](https://mlsanity.com/paper/2606.08303/) (2026)

## Abstract (arXiv)

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.