Benchmarking transfer learning in passive sonar: An evaluation study

Exploiting large-scale pre-trained models has proven effective in many domains, enabling limited data downstream tasks to be solved more effectively by leveraging upstream representations. However, transfer learning remains underexplored in passive sonar, where models are often trained from scratch using vision-oriented architectures on engineered features. We present the first systematic benchmark comparing transformer and CNN models across multiple passive sonar classification datasets. We evaluate six models across four sonar datasets, employing various fine-tuning strategies, including parameter-efficient Low-Rank Adaptation. Our findings demonstrate that full fine-tuning with audio pre-trained transformers performs most consistently. Fine-tuning on just 10% of the data can outperform training from scratch on the full dataset. Importantly, we introduce standardised training and evaluation protocols across multiple datasets, making conclusions more robust and replicable compared to prior work. This will inform both research and practice in passive sonar recognition.