DeepMind Blog
Nov 5, 2025
Mapping, modeling, and understanding nature with AI
AI models can help map species, protect forests and listen to birds around the world
How AI is helping advance the science of bioacoustics to save endangered species
Our new Perch model helps conservationists analyze audio faster to protect endangered species, from Hawaiian honeycreepers to coral reefs.
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arXiv Machine Learning
Jun 15
Beyond task performance: Decoding bioacoustic embeddings with speech features
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 AI
Jun 12
Decoding Insect Song: A Multitask Semisupervised Orthoptera Bioacoustic Classifier
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.
Hugging Face Trending Papers
Jul 15
MetaPerch: Learning from metadata for bioacoustics foundation models
Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data. Recent work has shown that supervision alone can produce SotA species detection models when trained on this large-scale data -- however, there remains unutilized potential in the form of recording metadata readily available within these community-driven data hubs.
arXiv Machine Learning
Jul 16
MetaPerch: Learning from metadata for bioacoustics foundation models
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 Machine Learning
4d ago
Beyond Discrimination: Calibrated Geoprior Fusion for Bioacoustic Monitoring
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...
arXiv AI
Jul 17
Can Tokens Compete? Token Representations against Supervised CNN Backbones for BirdCLEF+ 2026
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.
arXiv Machine Learning
Jul 7
BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations
arXiv:2605. 12534v3 Announce Type: replace-cross Abstract: Most work in audio enhancement targets human speech, while bioacoustics is less studied due to noisy recordings and the distinct traits of animal sounds.
arXiv Computer Vision
Sep 3
Efficient Passive Acoustic Monitoring of Killer Whales Using a Two-Stage Detection and Ecotype Classification Cascade
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.
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.
arXiv Machine Learning
Sep 15
BioDCASE: Active Learning for Bioacoustics
arXiv:2609.15255v1 Announce Type: new Abstract: Ecological monitoring increasingly relies on machine learning models, whose performance depends on the quality and quantity of labelled data. However,...
arXiv Machine Learning
Sep 24
EvoAudio: Recursive Self-Improvement for Audio Understanding
arXiv:2609.27389v1 Announce Type: cross Abstract: Audio language models understand what is said far better than how it sounds. Closing this gap takes more than data. Detailed acoustic annotation is c...