Hugging Face Trending Papers
BEANS-Next and ROOTS: Broadening Audio-Language Capabilities for Bioacoustics
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arXiv Machine Learning
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BEANS-Next and ROOTS: Broadening Audio-Language Capabilities for Bioacoustics
The paper introduces BEANS-Next, a comprehensive benchmark covering acoustic perception, biological category recognition, scene understanding, and in-context learning in bioacoustics, and shows that current audio‑language models perform poorly outside the narrow label‑centric tasks traditionally evaluated. To address this gap, the authors present ROOTS, a large‑scale training dataset enriched with curated real‑world data, behavioral and acoustic metadata, and synthetic generation, which improves performance across all BEANS‑Next task groups. Both the benchmark and dataset are released as open‑source resources to accelerate research in bioacoustics and ethology.
arXiv AI
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Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound
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Hugging Face Trending Papers
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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
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arXiv Machine Learning
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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
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.