The listening lab is an open-source platform for audio annotation of less vocal and typically more challenging species.
This tool allows you to:
- Upload raw field recordings
- Automatic segment sparse features of interest to reduce processing time
- Train state-of-the-art transformer-based model for your application
- Export annotations and models for use in the field
- Outlier detection
This tool was developed by the University of Canterbury's Listening Lab Bioacoustic Research group https://github.com/listening-lab
Data available here Invasive Species dataset
Start frontend using cd frontend then npm start after installing dependences
Start backend server using cd backend then uvicorn main:app --reload
Python dependences in ./backend/environment.yml
To start a clean build on your local machine run docker-compose up -d after cloning the repository.
Install the latest images using docker hub docker image pull -a benmcewen/listening-lab
Start the frontend using docker run -p 3000:3000 benmcewen/listening-lab:frontend and the backend using docker run -p 8000:8000 benmcewen/listening-lab:backend
We current use a transformer-based classification model - Audio Spectrogram Transformer (AST). The implementation can be found in ./backend/preprocessing/classifier.py.
If you find this tool useful, please cite it (journal publication coming soon!)
@article{mcewen2023,
title={An improved computational bioacoustic monitoring approach for detecting sparse features},
author={McEwen, Ben J and Soltero, Kaspar and Cone, Isaac and Gutschmidt, Stefanie and Bainbridge-Smith, Andrew and Atlas, James and Green, Richard},
journal={The Journal of the Acoustical Society of America},
volume={154},
number={4\_supplement},
pages={A143--A143},
year={2023},
publisher={AIP Publishing}
}
Feel free to contribute to this project or adapt it for your application.
- Set the model to automatically update prototypes
- Scale points in point map view
- Testing
- Prototype removed when label removed and unknown prototype not included at inference