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.
A deep convolutional neural network inspired by auditory perception for underwater acoustic target recognition
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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.