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.
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...
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."
arXiv:2606. 13236v1 Announce Type: cross Abstract: Passive acoustic monitoring holds great promise for ecological inference, yet existing automated tools are typically narrowly trained and non-transferable.
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...
arXiv:2607. 07733v1 Announce Type: cross Abstract: Passive hydroacoustic monitoring often generates large volumes of continuous recordings that are only partially exploited due to the cost of manual annotation.
arXiv:2606. 14662v1 Announce Type: new Abstract: Pretrained audio embeddings are standard in bioacoustics, yet little is known about which acoustic features these models encode, nor which are useful for a given task.
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.
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.
arXiv:2607. 06063v1 Announce Type: cross Abstract: Eco-acoustic monitoring generates vast volumes of audio data, making active learning a promising approach for reducing annotation effort while efficiently training reliable biodiversity classifiers.
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.
arXiv:2607. 14072v1 Announce Type: new Abstract: Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data.
arXiv:2607. 03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets.