← 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 Machine Learning
Sep 24

Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference

The paper discusses how active learning (AL) can alleviate the expert annotation bottleneck in biodiversity monitoring by selecting the most informative samples under a fixed budget. It highlights that while AL reduces labeling effort, its non-random sample selection complicates model validation, calibration, and ecological inference, issues often overlooked in current studies. The authors review existing AL research across acoustic and image data, identify gaps such as limited species coverage and lack of real-world deployments, and propose a tutorial framework and roadmap for developing AL methods that support efficient training, reliable validation, and trustworthy ecological conclusions.

By Ben McEwen, Shiqi Zhang, Dan Stowell
multimodalbenchmarks
More like this →
arXiv Machine Learning
Oct 5

Effects of interpulse-interval variation on deep-learning classification of bat vocalizations

The study examined how variation in the interpulse interval (IPI) of bat vocalizations affects deep‑learning classification. Two datasets—one preserving natural IPI timing and another normalizing call spacing to 50 ms—were used to fine‑tune EfficientNet‑B0 and PaSST models. Results showed that IPI normalization had mixed effects: PaSST performance remained stable while EfficientNet improved, yet models trained on natural IPI data performed better on natural test sets, indicating limited but non‑negligible influence of natural IPI variation.

By Welmoed R. Eversteijn, Burooj Ghani, A. Leonie Baier, Dan Stowell
llmsfine-tuning
More like this →
arXiv Computer Vision
Oct 12

CUBE: Class-conditioned Unknown Boundary Evidence for Open-Set Insect Species Recognition

CUBE introduces a class‑conditioned evidence framework for open‑set insect species recognition, modeling unknownness around each known species rather than as a single global unknown class. By evaluating test samples relative to their most similar known species, CUBE enables local rejection of visually confusable unknown insects and incorporates auxiliary species as shadow references and taxonomic candidate calibration. The extended CUBE‑E variant adds a lightweight location‑consistency term when ecological metadata is available, achieving state‑of‑the‑art AUROC scores on the Open‑Insect C‑America benchmark while preserving high closed‑set accuracy.

By Hao Tan, Qimin Zhang, Zhe Xiao, Zekai Zuo, Wanting Liu, Jinghui Qin
benchmarks
More like this →