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
CoarseSoundNet is a deep learning framework designed to classify ecological soundscapes into biophony, geophony, and anthropophony under realistic passive acoustic monitoring conditions. The study systematically explores model architectures, training data composition, and evaluation strategies, finding that adding more domain‑similar data, a silence class, and post‑hoc calibration improves performance. An ecological case study shows that pre‑filtering recordings with CoarseSoundNet produces acoustic index trends similar to ground‑truth filtering, though the impact on avian alpha‑diversity relationships is modest.
By Alexander Gebhard, Andreas Triantafyllopoulos, Dominik Arend, Sandra M\"uller, Svenja Schmidt, Michael Scherer-Lorenzen, Bj\"orn W. Schuller
arXiv:2609.15221v1 Announce Type: cross
Abstract: Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-spec...
By Tianyi Xu, Daniel Pimentel-Alarc\'on, Zuzana Bu\v{r}ivalov\'a, Claudia Sol\'is-Lemus
arXiv:2609.35863v1 Announce Type: cross
Abstract: Modern bioacoustic foundation models like Perch and BirdNET can identify species with high discriminative accuracy, yet their confidence scores are o...
By Neha Sajja, Bart van Merri\"{e}nboer, Burcu Karagol Ayan, Tom Denton
arXiv:2607. 03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets.
By Paria Vali Zadeh, Sven Tomforde
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.
By Burooj Ghani, Welmoed Eversteijn, Milan van Hirtum, Juan Sebasti\'an Ca\~nas, Vincent J. Kalkman, Dan Stowell, A. Leonie Baier