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arXiv · 2409.13878

Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained Models

Amirmohammad Mohammadi·Tejashri Kelhe·Davelle Carreiro·Alexandra Van Dine·Joshua Peeples
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Abstract

Transfer learning is commonly employed to leverage large, pre-trained models and perform fine-tuning for downstream tasks. The most prevalent pre-trained models are initially trained using ImageNet. However, their ability to generalize can vary across different data modalities. This study compares pre-trained Audio Neural Networks (PANNs) and ImageNet pre-trained models within the context of underwater acoustic target recognition (UATR). It was observed that the ImageNet pre-trained models slightly out-perform pre-trained audio models in passive sonar classification. We also analyzed the impact of audio sampling rates for model pre-training and fine-tuning. This study contributes to transfer learning applications of UATR, illustrating the potential of pre-trained models to address limitations caused by scarce, labeled data in the UATR domain.

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Amirmohammad Mohammadi, Tejashri Kelhe, Davelle Carreiro, Alexandra Van Dine, Joshua Peeples. 2024-09-20. Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained Models. https://arxiv.org/abs/2409.13878

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