Is monitoring biodiversity with acoustic indices a cul-de-sac?

This repository contains supporting code for manuscript article xxx

The audio dataset used in the paper is archived on Zenodo: (NOT PUBLISHED YET)

If the code, even partially, is used for other purpose please cite the article Haupert, S., Ducrettet, M., Sèbe, F., & Sueur, J. (2025). Is monitoring biodiversity with acoustic indices a cul-de-sac?. Journal xxx.

Setup and usage

All Python code was developed using a conda environment based on Python v3.10, but other versions may also work.

Download the .zip from Github (click on code then Download Zip) and extract all folders without changing the name of the folders neither rearrange the folder and sub-folders.

Then, download the audio dataset and the annotation file from Zenodo https://doi.org/10.5281/zenodo.14317014. Extract the .zip files in the directory data

Additional libraries required:

• tqdm
• scikit-maad
• scikit-learn
• seaborn

The correlation between acoustic indices was done with a specific notebook based on R v4.3.2 and the library :

• data.table
• corrplot