README.md

metadata

task_categories:
  - audio-classification
tags:
  - audio
  - bioacoustics
  - dolphin
  - whistle

Dolphin whistle classification

Short whistle clips with metadata, fundamental-frequency tracks, F0 spectrograms, and integer class IDs in main_category.

Whistle sub-category: fine-grained whistle identity is carried in the whistle_type column (integer IDs). This is the primary label column for sub-category work; main_category groups whistles at a coarser level.

Splits (train, validation, test) are disjoint by recording session.

Columns

• audio, main_category, name, onset, offset, duration, recording_duration, whistle_type, whistle_name, f0_time, f0_hz, f0_conf, f0_ok, f0_bad_reason, f0_spectrogram, snr_db

Usage

from datasets import load_dataset

# Default: one `datasets.Dataset` per split (`train`, `validation`, `test`)
ds = load_dataset("dolphinteam/Whistle-Classification")

Load all rows in a single dataset (no split separation; train, validation, and test concatenated in order):

from datasets import load_dataset

full = load_dataset("dolphinteam/Whistle-Classification", split="train+validation+test")

Equivalent if you already loaded the split dict and want one table:

from datasets import concatenate_datasets

ds = load_dataset("dolphinteam/Whistle-Classification")
full = concatenate_datasets([ds["train"], ds["validation"], ds["test"]])