Listening Lab - Annotator

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

Setup

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

Docker

To start a clean build on your local machine run docker-compose up -d after cloning the repository.

Docker Hub

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

Classification Pipeline

We current use a transformer-based classification model - Audio Spectrogram Transformer (AST). The implementation can be found in ./backend/preprocessing/classifier.py.

Citation

If you find this tool useful, please cite it

@article{mcewen2024active,
  title={Active few-shot learning for rare bioacoustic feature annotation},
  author={McEwen, Ben and Soltero, Kaspar and Gutschmidt, Stefanie and Bainbridge-Smith, Andrew and Atlas, James and Green, Richard},
  journal={Ecological Informatics},
  volume={82},
  pages={102734},
  year={2024},
  publisher={Elsevier}
}

Contribute

Feel free to contribute to this project or adapt it for your application.

Bug fixes

• Prototype removed when label removed and unknown prototype not included at inference