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Physics-Informed Learning for Robust Acoustic Localization with Calibrated Uncertainty — arXiv:2608.08911

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.08911
License (as archived): CC0-1.0
Archived: 2026-10-19T09:02:40.193Z
SHA-256 of the archived text: 8e2c88542f4ba91df41835f64f6c1e686c9df79160924d8ad44372b166af4191
Stable URL of this page: https://metraipolis.com/documento/dc_60cf191eeedc

How to cite

Physics-Informed Learning for Robust Acoustic Localization with Calibrated Uncertainty — arXiv:2608.08911 — arXiv (en), https://arxiv.org/abs/2608.08911 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_60cf191eeedc (Metraipolis archive dc_60cf191eeedc, sha256 8e2c88542f4ba91df41835f64f6c1e686c9df79160924d8ad44372b166af4191)

BibTeX

@misc{dc60cf191eeedc,
  title = {Physics-Informed Learning for Robust Acoustic Localization with Calibrated Uncertainty — arXiv:2608.08911},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.08911},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_60cf191eeedc; sha256 8e2c88542f4ba91df41835f64f6c1e686c9df79160924d8ad44372b166af4191},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.08911. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent advances in Passive Acoustic Monitoring (PAM) offer an opportunity to obtain ecological spatial point-process data at unprecedented scale. However, realizing this opportunity necessitates the development of accurate and scalable localization methods. In real-world outdoor soundscapes, however, the assumptions underlying classical localization methods such as hyperbolic and score-based localization are routinely violated by multipath dominance, near-field effects, and complex propagation. Under these conditions, classical localization methods become brittle, with extreme errors possible even in small detection arrays. Rather than statistically replacing the underlying physics, we propose a method to refine it and increase robustness outside of ideal operating conditions: a learned model operating on physics-informed acoustic features corrects a fast hyperbolic solver where it produces implausible solutions, substantially reducing catastrophic worst-case errors while matching its median accuracy on field data. We further provide calibrated, geometry-aware uncertainty estimates suitable for propagation into downstream spatial models. Evaluating on distributed microphone arrays in real and simulated outdoor environments, we demonstrate that the proposed method yields robust, uncertainty-aware localization, providing a step toward scalable automated wildlife monitoring in complex acoustic environments.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-11 in stat.ML).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG); Sound (cs.SD).
Pagina del lavoro: https://arxiv.org/abs/2608.08911 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:01:32.137Z dalla pagina https://arxiv.org/catchup/stat.ML/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).