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arXiv AI By Abhishek Jana, Moeumu Uili, James Atherton, Mark O'Brien, Joe Wood, Leandra Brickson

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

Read the original on arXiv AI →

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."

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 AI.

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