README.md

---
license: cc-by-4.0
task_categories:
  - audio-classification
language:
  - en
tags:
  - audio
  - birds
  - bioacoustics
  - biodiversity
  - ecology
  - population-estimation
  - bird-counting
  - zoo
  - aviary
  - passive-acoustic-monitoring
  - biodcase
  - biodcase2026
pretty_name: "BioDCASE 2026 - Bird Counting (Task 6)"
size_categories:
  - 100K<n<1M
---

# BioDCASE 2026 — Bird Counting (Task 6)

Development dataset for the **Bird Counting** task of the [BioDCASE 2026 Challenge](https://biodcase.github.io/challenge2026/).

## 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/](https://www.ml4biodiversity.org/biodcase26_birdcounts/)
- **Challenge page:** [https://biodcase.github.io/challenge2026/](https://biodcase.github.io/challenge2026/)
- **Baseline code:** [https://github.com/ml4biodiversity/biodcase-population-estimation](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](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 11.50 (MAPE 10.6%) 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](https://biodcase.github.io/challenge2026/).

## 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

```python
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:

```bash
# 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)](https://creativecommons.org/licenses/by/4.0/) license.

## Citation

If you use this dataset, please cite:

```bibtex
@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:

```bibtex
@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](https://biodcase.github.io/challenge2026/))
- **Aki Härmä** — Maastricht University
- **Aysenur Arslan-Dogan** — Maastricht University (main contact person)

Or open a discussion on the [dataset page](https://huggingface.co/datasets/Emreargin/BioDCASE2026_Bird_Counting/discussions).