A bio-inspired Gammatone-CNN framework achieves 98.41% accuracy on underwater acoustic target classification using cochleagram features from the VTUAD dataset.
A review of underwater target recognition based on deep learning
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Hearing the Ocean: Bio-inspired Gammatone-CNN framework for Robust Underwater Acoustic Target Classification
A bio-inspired Gammatone-CNN framework achieves 98.41% accuracy on underwater acoustic target classification using cochleagram features from the VTUAD dataset.