AI & Environment Resource HubRobinson, David; Miron, Marius; Hagiwara, Masato; Weck, Benno; Keen, Sara; Alizadeh, Milad; Narula, Gagan; Geist, Matthieu; Pietquin, Olivier · 2024-11-11
NatureLM-audio, a language-audio model for bioacoustics, advances species detection and conservation by setting new benchmarks in animal vocalization analysis.
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Robinson, David; Miron, Marius; Hagiwara, Masato; Weck, Benno; Keen, Sara; Alizadeh, Milad; Narula, Gagan; Geist, Matthieu; Pietquin, Olivier. 2024-11-11. NatureLM-Audio: an Audio-Language Foundation Model for Bioacoustics. https://arxiv.org/abs/2411.07186 (AI & Environment Resource Hub; record paper-034; collection snapshot 2026-10-04).
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Source sheet: Scientific Paper · Row 38 · Original ID: paper-034.
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Shared topics: Biodiversity and ecosystems
Improved machine learning techniques enhance bird call detection in passive acoustic monitoring, boosting precision and biodiversity tracking in climate research.
Shared topics: Biodiversity and ecosystems
This study proposes a low-cost IoT-based TinyML model that intelligently activates mosquito repellent only when an Anopheles mosquito is detected, using a 1D-CNN for efficient classification of mosquito wingbeat sounds, reducing energy waste and potential health risks.
Shared topics: Biodiversity and ecosystems
A hybrid machine learning model improves soil respiration predictions, enhancing carbon cycle modeling and greenhouse gas management strategies.
Shared topics: Biodiversity and ecosystems
A deep learning-based bioacoustic AI tool enables citizen scientists and law enforcement to accurately identify traded songbird species in Asian markets, achieving 91.6% accuracy and supporting conservation efforts against overexploitation and illegal wildlife trade.