dolphinteam/OpenWhistle-CNN

OpenWhistle CNN Dataset

dolphinteam/OpenWhistle-CNN is the public CNN dataset used for binary dolphin whistle detection. It contains audio windows, spectrogram images, and binary labels:

• noise (label=0)
• whistle (label=1)

The default config is the complete session-disjoint dataset used for training and evaluation.

Dataset contents

• Hugging Face repo: dolphinteam/OpenWhistle-CNN
• Public columns: audio, spectrogram, label, file_name, recording, onset, offset
• Audio: 0.4 s mono windows at 96 kHz

Dataset splits

Split	Rows	Noise	Whistle	Sessions	Window hours
train	53,828	26,914	26,914	195	5.980885
validation	5,980	2,990	2,990	26	0.664445
test	16,708	8,354	8,354	261	1.856444
Total	76,516	38,258	38,258	482	8.501775

The train and validation splits come from the non-2019/2020 pool. The test split is a manual 2019-2020 test split built from the unbalanced config of dolphinteam/OpenWhistle-Classification-Finetuning (named all in earlier revisions of that repository).

Loading the data

from datasets import load_dataset

dataset = load_dataset("dolphinteam/OpenWhistle-CNN")

Earlier revisions

Earlier revisions of this repository also contained a 480-example review-sample config for inspection. It remains available at revision 49242e751ac57b2cc7b8131a86fd9c1497cfcddb:

from datasets import load_dataset

review = load_dataset(
    "dolphinteam/OpenWhistle-CNN",
    "review-sample",
    revision="49242e751ac57b2cc7b8131a86fd9c1497cfcddb",
)

License

This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0), as recorded in its Croissant/RAI metadata.

Paper, collection, and authors

• Paper: OpenWhistle: A Large-Scale Longitudinal Dataset and Benchmark of Bottlenose Dolphin Vocalizations
• Hugging Face collection: NeurIPS 2026 OpenWhistle
• This repository: https://huggingface.co/datasets/dolphinteam/OpenWhistle-CNN

Authors: Faadil Mustun, Chiara Semenzin, Roberto Dessi, Pablo Robin Guerrero, Pierre Orhan, Alexis Emanuelli, Emanuele Rossi, Yair Lakretz, Gonzalo de Polavieja, and German Sumbre.

Citation

@misc{mustun2026openwhistle,
  title         = {OpenWhistle: A Large-Scale Longitudinal Dataset and Benchmark of Bottlenose Dolphin Vocalizations},
  author        = {Mustun, Faadil and Semenzin, Chiara and Dessi, Roberto and Robin Guerrero, Pablo and Orhan, Pierre and Emanuelli, Alexis and Rossi, Emanuele and Lakretz, Yair and de Polavieja, Gonzalo and Sumbre, German},
  year          = {2026},
  eprint        = {2609.34839},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2609.34839}
}