OpenWhistleNeurIPS26/OpenWhistle-CNN

OpenWhistle CNN Dataset

OpenWhistleNeurIPS26/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 main dataset is the complete session-disjoint dataset used for training and evaluation. A smaller deterministic review-sample config is also provided so reviewers can inspect representative examples quickly.

Dataset contents

• —Hugging Face repo: OpenWhistleNeurIPS26/OpenWhistle-CNN
• —Public columns: audio, spectrogram, label, file_name, recording, onset, offset

Full dataset splits

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 full classification all config.

Review sample

The review-sample config is a small deterministic subset of the same public dataset. It was created only to make review and manual inspection easier. It is not a replacement for the full dataset used for model development or reporting.

How the review sample was created

The review sample was designed to preserve the structure of the full dataset while keeping the download small enough for quick manual inspection. The sample keeps the same binary label definition as the full dataset and preserves the train/test separation: reviewer training examples are drawn from the original training and validation data, while reviewer test examples are drawn only from the original test data.

Within each reviewer split, examples were sampled separately for noise (label=0) and whistle (label=1) so that both classes are equally represented. This avoids a reviewer sample dominated by one class and makes it easier to inspect positives and negatives side by side. The target sizes were chosen to keep the same approximate train/test ratio as the full CNN dataset: 376 examples for train and 104 examples for test, for 480 examples total.

Sampling was deterministic, using seed 42, so the same review sample can be rebuilt exactly from the prepared public dataset. The resulting config is named review-sample.

Review sample size

Loading the data

from datasets import load_dataset

full = load_dataset("OpenWhistleNeurIPS26/OpenWhistle-CNN")
review = load_dataset("OpenWhistleNeurIPS26/OpenWhistle-CNN", "review-sample")