Large empirical study finds self-supervised pre-training then supervised post-training on mixed bioacoustics and general audio data produces the strongest encoders across 26 datasets for species classification, detection, individual ID and repertoire discovery.
The watkins marine mammal sound database: An online, freely accessible resource
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MT-BCA-CNN achieves 97% accuracy and 95% F1-score on 27-class few-shot underwater acoustic target recognition by combining channel attention and multi-task learning on the Watkins Marine Life Dataset.
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AVEX: What Matters for Animal Vocalization Encoding
Large empirical study finds self-supervised pre-training then supervised post-training on mixed bioacoustics and general audio data produces the strongest encoders across 26 datasets for species classification, detection, individual ID and repertoire discovery.
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A Multi-task Learning Balanced Attention Convolutional Neural Network Model for Few-shot Underwater Acoustic Target Recognition
MT-BCA-CNN achieves 97% accuracy and 95% F1-score on 27-class few-shot underwater acoustic target recognition by combining channel attention and multi-task learning on the Watkins Marine Life Dataset.