This is the implementation of our paper accepted at ICASSP 2024
We improve over our previous work that ranked 2nd in the DCASE 2023 Challenge Task5 by adding a regularization term to the training loss, and by improving the inference strategy
args.py
: contains default values for arguments
Download the dataset from Zenodo: https://zenodo.org/records/6482837
To create the spectrograms of the training set:
create_train.py
: with argument --traindir
for the folder containing the training datasets
To train the feature extractor:
train.py
: with arguments --traindir
(the same as above)
--method
: optional argument for the pretraining method scl
for SupCon and ssl
for SimCLR
To evaluate using finetuning:
eval_finetune
: with arguments --valdir
for the folder containing the validation datasets
To evaluate without finetuning (for faster inference):
eval_nofinetune
: with arguments --valdir
for the folder containing the validation datasets
To get the scores:
evaluation.py
: with arguments --pred_file
for the predictions csv file created by the eval script (the file is in : traindir/../../outputs/eval.csv'), --ref_files
for the path of validation datasets (same as --valdir
), and --save_path
for the folder where to save the json file containing the scores
If you any question or a problem with the code/results do not hesitate to mail me on : [email protected] or open an issue on this repository, I am very responsive.
@misc{moummad2023regularized,
title={Regularized Contrastive Pre-training for Few-shot Bioacoustic Sound Detection},
author={Ilyass Moummad and Romain Serizel and Nicolas Farrugia},
year={2023},
eprint={2309.08971},
archivePrefix={arXiv},
primaryClass={cs.SD}
}