Repertoire Embedder

Description

Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.

Badges

On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.

Visuals

Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.

For a detailled description of the scientific motivation and experiments corresponding to this repository, please see Best, P., Marxer, R., Paris, S., & Glotin, H. (2023). Deep audio embeddings for vocalisation clustering. bioRxiv, 2023-03.

Installation

Necessary python packages can be installed using conda with the environment.yml file, or with pip using requirements.txt files in their corresponding folders.

Usage

The paper_experiments folder contains scripts and data that were used in the published paper. Data to reproduce experiments can be found on this figshare repository

The new_specie folder contains scripts to run auto-encoder embeddings and cluster your own dataset of animal vocalisations. Scripts allow you to train your own auto-encoder, but a generic pretrained encoder is usually suffice (see article).

License

When using this code in your own experiments, please cite the corresponding paper.