← Back to all news
arXiv Machine Learning By Burooj Ghani, Welmoed Eversteijn, Milan van Hirtum, Juan Sebasti\'an Ca\~nas, Vincent J. Kalkman, Dan Stowell, A. Leonie Baier

ChiroEcho: extending automated bat vocalisation classification beyond the learned taxonomy

Read the original on arXiv Machine Learning →

ChiroEcho is a deep learning framework that jointly predicts bat species and genus, then uses genus predictions together with geographic species distributions to identify species not present in the training taxonomy. By incorporating geographic constraints, the system expands its effective taxonomy, enabling classification of 41 out of 48 native European bat species—an increase from 73% to 85% coverage. The study demonstrates that limited evaluation data can mask species‑level performance and that combining coarse predictions with external constraints can recover labels for unseen fine‑grained classes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
6d ago

An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeon

This paper introduces an automated one‑shot bird call classification pipeline tailored for rare species that lack large training datasets. By leveraging embedding spaces from large bird classification networks and a cosine‑similarity classifier, the system incorporates filtering and denoising steps to detect calls with minimal data. The approach was validated on simulated Xeno‑Canto recordings and on the critically endangered tooth‑billed pigeon, achieving 1.0 recall and 0.95 accuracy. "whyItMatters":"The system enables conservationists to monitor endangered species with only a few recordings, filling a gap left by existing large‑scale classifiers."

By Abhishek Jana, Moeumu Uili, James Atherton, Mark O'Brien, Joe Wood, Leandra Brickson
rag
More like this →
Hugging Face Trending Papers
Jul 15

MetaPerch: Learning from metadata for bioacoustics foundation models

Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data. Recent work has shown that supervision alone can produce SotA species detection models when trained on this large-scale data -- however, there remains unutilized potential in the form of recording metadata readily available within these community-driven data hubs.

benchmarks
More like this →