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
The paper presents an integrated framework for processing and classifying invasive bird species vocalizations in noisy natural soundscapes. It uses Bayesian wavelet shrinkage with an Epanechnikov kernel prior to denoise signals, then extracts features such as MFCCs and spectral indices. Supervised models—including Random Forest, Multinomial Logistic Regression, and SVM—are evaluated, with the SVM achieving the highest accuracy (0.9398) on a 10‑dimensional MFCC set.
By Laura Lucia Dominguez Barrios, Fidel Aniano Causil Barrios, Alex Rodrigo dos Santos Sousa, Mariana Rodrigues Motta
arXiv:2607. 14474v1 Announce Type: cross Abstract: This paper details the DS@GT ARC team's approach to BirdCLEF+ 2026, multi-label detection of animal vocalizations in soundscapes from the Pantanal wetlands.
By Anthony Miyaguchi, Murilo Gustineli, Adrian Cheung
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
arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.
By Maris Basha, Anja Zai, Sabine Stoll, Richard Hahnloser
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
The paper presents a lightweight ResNet-based two-stage cascade for passive acoustic monitoring of killer whales. First, it detects vocalizations, then it classifies confident detections into five eastern North Pacific ecotypes, abstaining on ambiguous calls. The pipeline achieves high macro‑F1 scores on the DCLDE 2027 dataset and improves real‑time inference speed, while active learning adapts the detector to new acoustic environments.
By Daniela Ruiz, Manuel Castellote, Zhongqi Miao, Carl Chalmers, Bruno Demuro, Rahul Dodhia, Pablo Arbelaez, Juan M. Lavista
The paper explores how the number of bird species (target classes) affects the compressibility of neural networks for passive acoustic monitoring on microcontroller units (MCUs). By training and compressing models with varying class counts, the authors show that significant compression can be achieved with minimal performance loss. They also benchmark different hardware platforms and assess the feasibility of deploying energy‑autonomous monitoring devices.
By Nina Brolich, Simon Geis, Maximilian Kasper, Alexander Barnhill, Axel Plinge, Dominik Seu{\ss}
arXiv:2603.04163v2 Announce Type: replace
Abstract: Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based...
By Thanos Polychronou, Luk\'a\v{s} Adam, Viktor Penchev, Kostas Papafitsoros
The paper introduces a new benchmark dataset of over 6,000 high‑resolution images for detecting and segmenting Western bluebirds in natural, cluttered scenes. Experiments show that supervised detectors such as Faster R‑CNN and RT‑DETR outperform open‑vocabulary models in zero‑shot settings, though fine‑tuned YOLO‑World can compete. Segmentation results favor Mask R‑CNN for mask quality, while YOLOv8‑Seg offers the best precision and speed. The study also identifies multiple factors—scale, brightness, contrast, clutter, blur, crowding, and session variation—as contributors to detection failures.
By Estela Monserrat Arriaga Santana, Julian Rosas Scull, Ibeth P. Alarc\'on, Bibiana Montoya, Aylin Sosa Mej\'ia, Hugo Jair Escalante
arXiv:2608.23101v1 Announce Type: cross
Abstract: Recent progress in the field of TinyML has demonstrated that low-power hardware based on microcontrollers can achieve bird species monitoring in real...
By Nathan Duboisset, Zhaolan Huang, Felix Bie{\ss}mann, Roudy Dagher, Antoine Lavandier, Emmanuel Baccelli
The study presents a deep‑learning object detection framework that identifies products derived from threatened species—elephant, pangolin, and tiger—using real‑world images from online marketplaces, enforcement seizures, and market inspections. Trained on 39,941 images, the best model achieved 84.2% overall accuracy, with 93.6% for pangolin, 90.3% for tiger, and 71.1% for elephant. The optimized model was deployed in a prototype smartphone app, which reached 91.3% accuracy and an F‑score of 0.86 on an independent test set, demonstrating its potential for real‑time monitoring of wildlife trade.
By Ritwik Kulkarni, WU Hanqin, Enrico Di Minin