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.
Related stories
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 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 Machine Learning
Jul 8
Determinantal point process sampling for bioacoustic active learning
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.
arXiv Machine Learning
Jul 10
A Self-Supervised Approach for Minimal-Annotation Hydroacoustic Data Exploration
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 AI
Jun 16
EChO-Agent: Evidence Chain Orchestration Agent for Audio Reasoning
arXiv:2606. 15141v1 Announce Type: cross Abstract: While LALMs show promise on audio question answering, they fail to focus on question-relevant segments of audio and provide a clear, checkable reasoning process when dealing with complex audio reasoning.
arXiv Machine Learning
Jul 7
Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types
arXiv:2607. 03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets.
OpenAI Blog
May 7
Advancing voice intelligence with new models in the API
Explore new realtime voice models in the OpenAI API that can reason, translate, and transcribe speech, enabling more natural and intelligent voice experiences.