OceanBEATs

OceanBEATs is a foundation model for underwater acoustic monitoring, adapted from BEATs via Domain-Adaptive Pretraining (DAPT) on approximately 4,400 hours of global ocean soundscapes (World-DAPT corpus).

This model serves as the "ears" for underwater soundscapes described in our paper: "A stethoscope for the ocean: Unknownness-aware monitoring under false-positives-per-hour constraints in underwater soundscapes" (Under Review, Scientific Reports).

Model Details

• Model Type: Audio Transformer (BEATs architecture)
• Pretraining: Masked Audio Modeling + DAPT (SimCLR/InfoNCE) on underwater data
• Input: 16kHz mono audio waveform
• Backbone: BEATs AS-2M (iter3+)

Available Files

This repository hosts the pretrained weights required to reproduce the results in our paper.

1. beats_dapt_topup_encoder.pt

   • The core encoder (backbone) adapted to underwater acoustics.
   • Use this for feature extraction, unknown detection (CCED2), or fine-tuning on new marine datasets.

2. sed_head_56_topup_ep8.pt

   • A 56-class Sound Event Detection (SED) head trained on coastal/lagoon data (Okinawa, Japan).
   • Detects fish, mammals, vessels, and environmental sounds.

Usage

These weights are designed to be used with the official code repository:

GitHub Repository: alohajazz/openworld-soundscape-cced2-dgpu

Please download the .pt files and place them in the weights/ directory of the cloned GitHub repository.

# Example directory structure after download
openworld-soundscape-cced2-dgpu/
└── weights/
    ├── beats_dapt_topup_encoder.pt
    ├── sed_head_56_topup_ep8.pt
    └── cced2/ ...

License & Data Availability

License: CC BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0 International)

These weights are released for non-commercial research purposes only. Commercial use is strictly prohibited without prior permission from the authors.

Note: The source code for using these models is released under the MIT License at the GitHub repository linked above.

Citation

If you use this model in your research, please cite our paper:

@article{noda2026stethoscope,
  title={A stethoscope for the ocean: Unknownness-aware monitoring under false-positives-per-hour constraints in underwater soundscapes},
  author={Noda, Takuji and Koizumi, Takuya},
  journal={Scientific Reports},
  note={Under Review},
  year={2026}
}

Acknowledgements

The base model architecture is based on BEATs (Microsoft). We acknowledge the creators of the BEATs model and the various open-source ocean acoustic datasets (SanctSound, ONC, PALAOA, etc.) used for DAPT.