Supervised Contrastive Learning for Pre-Training Bioacoustic Few-Shot Systems

Authors: Ilyass Moummad, Romain Serizel, Nicolas Farrugia

This is the implementation of our submission for the challenge DCASE 2023 task 5.
We invite you to take a look at the workshop paper version for more details and ablation studies.
We ranked 2nd in the challenge. For more informations about the challenge results, click here

Our approach consists in :

• Training a feature extractor on the training set
• Training a linear classifier on each audio of the validation set

Firstly, we 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), --device the device to train on, and others concerning training and data augmentation hyperparameters that can be found in args.py with default values that we used.

To validate the learned feature extractor using 5-shots :
evaluate.py : with arguments --valdir for the folder containing the validation datasets, and others concerning hyperparameters that can also be found in args.py. Add :
For submission 1 :
--ft 0 --ftlr 0.01 --ftepochs 20 --method ce --adam
For submission 2 :
--ft 1 --ftlr 0.001 --ftepochs 40 --method ce --adam
For submission 3 :
--ft 2 --ftlr 0.001 --ftepochs 40 --method ce --adam
For submission 4 :
--ft 3 --ftlr 0.001 --ftepochs 40 --method ce --adam

To get the scores :
evaluation_metrics/evaluation.py : with arguments -pred_file for the predictions csv file created by evaluate.py (the file is in : traindir/../../outputs/eval.csv'), -ref_files for the path of validation datasets, and -save_path for the folder where to save the scores json file

If you any question or a problem with the code/results do not hesitate to mail me on : ilyass.moummad@imt-atlantique.fr or open an issue on this repository, I am very responsive.

---

Credits

We are thankful for the challenge baseline code that helped us make this repository : https://github.com/ilyassmoummad/dcase-few-shot-bioacoustic/tree/main

Bonus

Take a look at our newer work accepted at ICASSP 2024 where we improve the pre-training loss as well as the inference strategy (which is more stable) : https://github.com/ilyassmoummad/RCL_FS_BSED

---

To cite the challenge report

@techreport{Moummad2023,
    Author = "Moummad, Ilyass and Serizel, Romain and Farrugia, Nicolas",
    title = "SUPERVISED CONTRASTIVE LEARNING FOR PRE-TRAINING BIOACOUSTIC FEW SHOT SYSTEMS",
    institution = "DCASE2023 Challenge",
    year = "2023",
    month = "June",
}

Or to cite the workshop paper version

@inproceedings{moummad,
    author = "Moummad, Ilyass and Serizel, Romain and Farrugia, Nicolas",
    title = "Pretraining Representations for Bioacoustic Few-Shot Detection Using Supervised Contrastive Learning",
    booktitle = "Proceedings of the 8th Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023)",
    address = "Tampere, Finland",
    month = "September",
    year = "2023",
    pages = "136--140",
}