Emreargin/BioDCASE2026_Bird_Counting

BioDCASE 2026 — Bird Counting (Task 6)

Development dataset for the Bird Counting task of the BioDCASE 2026 Challenge.

Task overview

Estimating the number of individual birds from acoustic recordings is a fundamental challenge in biodiversity monitoring. This task addresses bird abundance estimation in zoo aviaries with known ground-truth population counts.

Participants receive collections of short audio fragments (~3 seconds each) extracted from continuous passive acoustic recordings in multi-species aviaries. Each aviary contains a known number of a target bird species alongside other co-occurring species. The recordings capture birds vocalizing naturally in groups over extended periods, creating realistic acoustic complexity including overlapping vocalizations, environmental noise, and natural behavioral variation.

The task is to estimate the number of individuals of the target species in each aviary.

For full task details, timeline, evaluation criteria, and submission instructions, see:

• Task page: https://www.ml4biodiversity.org/biodcase26_birdcounts/
• Challenge page: https://biodcase.github.io/challenge2026/
• Baseline code: https://github.com/ml4biodiversity/biodcase-population-estimation

Dataset description

The development dataset contains 140,899 audio files across 6 aviaries recorded at European zoos using passive acoustic monitoring equipment. Recordings were made during spring and summer 2025. Each aviary was recorded continuously for 7–11 days; this dataset includes a curated subset of 2–3 representative days per aviary selected to minimize distributional distortion of key acoustic features while keeping the dataset manageable.

Target species

Three bird species are designated as estimation targets. Population estimation is evaluated only for these species:

Species	Scientific name	Aviaries	Population range
Greater flamingo	Phoenicopterus roseus	dev_aviary_2, dev_aviary_4, dev_aviary_5, dev_aviary_6	52–161
Red-billed quelea	Quelea quelea	dev_aviary_1, dev_aviary_3	61–153
Hadada ibis	Bostrychia hagedash	dev_aviary_2, dev_aviary_4	4–6

Each aviary also contains additional non-target bird species (2–12 species per aviary, 28 species in total across all aviaries). The complete species inventory with population counts is provided in metadata/ground_truth.csv.

Aviary summary

Aviary	Days	Audio files	Target species	Target population
dev_aviary_1	3	12,627	Red-billed quelea	153
dev_aviary_2	3	25,569	Greater flamingo (107), Hadada ibis (6)	113
dev_aviary_3	3	11,879	Red-billed quelea	61
dev_aviary_4	3	36,340	Greater flamingo (161), Hadada ibis (4)	165
dev_aviary_5	2	19,363	Greater flamingo	52
dev_aviary_6	3	35,121	Greater flamingo	52
Total	17	140,899

Note: Aviary 5 and aviary 6 are two separate recording sessions from the same physical location with the same bird population, captured on different dates. They are treated as independent data points with different acoustic conditions.

Audio format

All audio files are single-channel (mono) WAV files, 16-bit PCM, sampled at 48 kHz, with a duration of approximately 3 seconds each. The files represent consecutive, non-overlapping segments extracted from continuous recordings.

Dataset structure

BioDCASE2026_Bird_Counting/
├── dev_aviary_1/
│   ├── chunk_000/
│   │   ├── rec_d1_00_00_45.750000.wav
│   │   ├── rec_d1_00_01_49.wav
│   │   └── ...
│   ├── chunk_001/
│   │   └── ...
│   └── ...
├── dev_aviary_2/
│   └── ...
├── dev_aviary_3/
│   └── ...
├── dev_aviary_4/
│   └── ...
├── dev_aviary_5/
│   └── ...
├── dev_aviary_6/
│   └── ...
└── metadata/
    ├── ground_truth.csv
    └── recording_info.csv

Filename convention

Audio filenames follow the pattern:

rec_{day}_{HH}_{MM}_{SS}[.ffffff].wav

where {day} is a day identifier (d1, d2, or d3) and {HH}_{MM}_{SS}[.ffffff] encodes the time of day (hours, minutes, seconds, optional fractional seconds). For example, rec_d1_19_05_02.500000.wav is a recording from day 1 at 19:05:02.5.

Day identifiers are anonymized — the mapping from day identifiers to calendar dates is not provided to participants.

Chunk subdirectories

Within each aviary, audio files are organized into chunk_NNN/ subdirectories for practical file management. The chunk boundaries have no acoustic significance — they are simply a way to keep directory sizes manageable. All chunks within an aviary should be treated as a single continuous collection.

Metadata

metadata/ground_truth.csv

Complete species inventory for all 6 aviaries, including both target and non-target species:

Column	Description
aviary_id	Aviary identifier (dev_aviary_1 through dev_aviary_6)
common_name	English common name of the species
scientific_name	Binomial scientific name
count	Number of individuals present in the aviary
is_target	1 if the species is evaluated for population estimation, 0 otherwise

metadata/recording_info.csv

Summary statistics per aviary:

Column	Description
aviary_id	Aviary identifier
n_days	Number of recording days included
n_files	Total number of audio files

Baseline system

A complete baseline system is available at https://github.com/ml4biodiversity/biodcase-population-estimation. It implements a two-stage pipeline:

1. Species detection — Run a bird species detector on each aviary's audio files. Two detection packages are provided:

   • pip install aria-inference (ARIA ensemble detector, recommended)
   • pip install aria-inference-birdnet (BirdNET-based detector)

2. Feature extraction — Extract detection-count statistics, temporal bout structure, and optionally scikit-maad acoustic indices from the detection output.

3. Population estimation — Fit species-specific regression models using leave-one-out cross-validation.

The baseline achieves a combined MAE of 11.77 (MAPE 11.4%) across all target species using ARIA detections.

Evaluation

The main leaderboard ranks systems based on population estimation accuracy for the three target species. The primary metric is Mean Absolute Error (MAE) computed across all (aviary, target species) data points. Secondary metrics include RMSE, R², and MAPE.

Participants may optionally extend their methods to non-target species for a secondary leaderboard, but this does not affect final rankings.

The evaluation set will be released according to the challenge timeline.

Key challenges

• Flock-calling species: Greater flamingos vocalize synchronously in large groups, making it difficult to distinguish individual contributions from detection counts alone. Raw detection rates saturate as flock size grows.
• Sparse calibration data: With only 6 aviaries (and 2–4 data points per target species), models must generalize from very few examples.
• Multi-species environments: Each aviary contains 2–12 co-occurring species with overlapping frequency ranges and calling times.
• Population range: Target populations span two orders of magnitude (4 to 161 individuals), requiring methods that work across scales.

Usage with 🤗 Datasets

from datasets import load_dataset

# Load the dataset (streams audio on demand)
ds = load_dataset("Emreargin/BioDCASE2026_Bird_Counting")

Or download directly and process locally:

# Clone with git-lfs
git lfs install
git clone https://huggingface.co/datasets/Emreargin/BioDCASE2026_Bird_Counting

# Run the baseline
cd biodcase-population-estimation
pip install aria-inference
aria-inference --input ../BioDCASE2026_Bird_Counting/dev_aviary_1/ --output detections/dev_aviary_1_detections.csv
# ... repeat for dev_aviary_2 through dev_aviary_6
python feature_builder.py --detections-dir detections/ --audio-root ../BioDCASE2026_Bird_Counting/ --output results/stage2_features.csv
python estimator.py --features results/stage2_features.csv

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Citation

If you use this dataset, please cite:

@dataset{ml4biodiversity2026dataset,
  author       = {Arg{\i}n, Emre and H{\"a}rm{\"a}, Aki and Arslan-Dogan, Aysenur},
  title        = {{BioDCASE 2026 Bird Counting: Avian Population Estimation
                   from Passive Acoustic Recordings}},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/Emreargin/BioDCASE2026_Bird_Counting},
}

Please cite this repository if you use the official baseline implementation:

@software{ml4biodiversity2026baseline,
  author       = {Arg{\i}n, Emre and H{\"a}rm{\"a}, Aki and Arslan-Dogan, Aysenur},
  title        = {{BioDCASE 2026 Bird Counting Baseline: Avian Population Estimation
                   from Passive Acoustic Recordings}},
  year         = {2026},
  publisher    = {GitHub},
  url          = {https://github.com/ml4biodiversity/biodcase-population-estimation},
  version      = {1.0.0},
}

Contact

For questions about the dataset or the challenge task, please contact:

• Emre Argın — Maastricht University (challenge task lead)
• Aki Härmä — Maastricht University
• Aysenur Arslan-Dogan — Maastricht University (main contact person)

Or open a discussion on the dataset page.