Transfer Learning for Passive Sonar Classification using Pre-trained Audio and ImageNet Models
<|reference_start|>Transfer Learning for Passive Sonar Classification using Pre-trained Audio and ImageNet Models: 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.<|reference_end|>
@article{mohammadi2024transfer,
    title={Transfer Learning for Passive Sonar Classification using Pre-trained
  Audio and ImageNet Models},
    author={Amirmohammad Mohammadi, Tejashri Kelhe, Davelle Carreiro, Alexandra
  Van Dine, Joshua Peeples},
    journal={arXiv preprint arXiv:2409.13878},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13878},
    primaryClass={cs.SD cs.LG eess.AS}
}