Datasets:
Tasks:
Audio Classification
Modalities:
Audio
Formats:
soundfolder
Languages:
English
Size:
10K - 100K
License:
| license: cc-by-nc-sa-4.0 | |
| task_categories: | |
| - audio-classification | |
| language: | |
| - en | |
| tags: | |
| - acoustic-detection | |
| - drone | |
| - military-audio | |
| - environmental-sound | |
| pretty_name: STRIX Dataset | |
| size_categories: | |
| - 1K<n<10K | |
| # dataset_strix | |
| Dataset audio unifié pour le projet STRIX (Proteus Group, association étudiante) — | |
| classification de sons en zone de conflit : gunshot, explosion, vehicle, drone, background. | |
| ## Format | |
| - 16 kHz, mono, clips de 2 secondes | |
| - Structure : `train/<classe>/` et `test/<classe>/` | |
| - Classes : gunshot, explosion, vehicle, drone, background | |
| ## Sources et attributions | |
| - **MAD (Military Audio Dataset)** — June Wook Kim, Kaggle. | |
| Licence : CC BY-SA 4.0. | |
| https://www.kaggle.com/datasets/junewookim/mad-dataset-military-audio-dataset | |
| - **ESC-50** — Piczak, K. J. (2015). ESC: Dataset for Environmental Sound | |
| Classification. Licence CC BY-NC 3.0. | |
| https://github.com/karoldvl/ESC-50 | |
| - **DroneAudioDataset** — Al-Emadi, S. et al. (2019). Audio Based Drone Detection | |
| and Identification using Deep Learning. IWCMC 2019 Vehicular Symposium, Tangier, Morocco. | |
| https://github.com/saraalemadi/DroneAudioDataset | |
| Ce dataset combinant des sources sous CC BY-SA 4.0 et CC BY-NC 3.0, il est distribué | |
| sous la licence la plus restrictive applicable (CC BY-NC-SA 4.0) : usage non-commercial, | |
| attribution requise, partage à l'identique pour les dérivés. | |
| ## Pipeline de préparation | |
| Voir `STRIX_preparation_dataset.ipynb` : standardisation 16kHz/mono/2s, fenêtrage | |
| glissant, mapping de labels MAD vers les 5 classes STRIX. |