Transfer Learning for Passive Sonar Classification using Pre-trained Audio and ImageNet Models

• URL: http://arxiv.org/abs/2409.13878v1
• Date: Fri, 20 Sep 2024 20:13:45 GMT
• Title: Transfer Learning for Passive Sonar Classification using Pre-trained Audio and ImageNet Models
• Authors: Amirmohammad Mohammadi, Tejashri Kelhe, Davelle Carreiro, Alexandra Van Dine, Joshua Peeples,
• Abstract summary: This study compares pre-trained Audio Neural Networks (PANNs) and ImageNet pre-trained models. It was observed that the ImageNet pre-trained models slightly out-perform pre-trained audio models in passive sonar classification.
• Score: 39.85805843651649
• License: http://creativecommons.org/licenses/by/4.0/
• 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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