Dolph2Vec is the first species-specific self-supervised model for dolphin vocalizations, trained on longitudinal recordings from five dolphins, that outperforms general baselines on signature whistle classification and detection while producing embeddings aligned with known whistle categories.
Aves: Animal vocalization encoder based on self-supervision
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Meow-Omni 1 is a quad-modal MLLM that fuses video, audio, physiological time-series, and text to achieve 71.16% accuracy on feline intent recognition in the new MeowBench benchmark.
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Dolph2Vec: Self-Supervised Representations of Dolphin Vocalizations
Dolph2Vec is the first species-specific self-supervised model for dolphin vocalizations, trained on longitudinal recordings from five dolphins, that outperforms general baselines on signature whistle classification and detection while producing embeddings aligned with known whistle categories.
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Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology
Meow-Omni 1 is a quad-modal MLLM that fuses video, audio, physiological time-series, and text to achieve 71.16% accuracy on feline intent recognition in the new MeowBench benchmark.