---
license: cc-by-4.0
pretty_name: Mosquitoes — BioDCASE 2026 Task 5 (CD-MSC) caches
task_categories:
  - audio-classification
language:
  - en
tags:
  - bioacoustics
  - mosquito
  - wingbeat
  - domain-generalization
  - biodcase
size_categories:
  - 100K<n<1M
---

# Mosquitoes — precomputed caches for BioDCASE 2026 Task 5 (CD-MSC)

Data backend for the [**Mosquitoes**](https://github.com/aptemvs) code repo (cross-domain
mosquito species classification, BioDCASE 2026 Task 5). It holds the raw audio plus the
**derived caches** the pipeline consumes, so a clone can reproduce every result without
recomputing embeddings. The Hub layout mirrors the code repo; `fetch_data.py` pulls a group
into place:

```bash
python fetch_data.py --group deployed   # ~3.7 GB — light probes + agreement gate
python fetch_data.py --group all        # HF caches (~58 GB) + Zenodo raw audio (~6.3 GB)
```

## Contents

This HF dataset holds the **derived caches** (~58 GB). The **raw audio is not re-hosted here**
— it is the official challenge dataset and is downloaded from Zenodo by `fetch_data.py` when
you request `--group heavy`/`all` (see *Provenance* below).

| group | size | files | what it is |
|---|---|---|---|
| `deployed` | 3.7 GB | 78 | Perch / harmonic / bg-whitened embeddings (`data/perch/*.npz`), BirdMAE parquet, split metadata — runs the light probes + agreement gate |
| `repro` | 9.5 GB | 1,920 | `legacy/outputs` + `legacy/final` (checkpoints, `ensemble.json`) — replays the historical leaderboard via `import_runs.py` / `train_gate.py` |
| `heavy` | 19.6 GB | 756 | the log-mel streaming cache (`data/feature`) for the MTRCNN/EfficientAT path (raw audio comes from Zenodo) |
| `extras` | 25.3 GB | 14 | additional foundation-model embeddings (Perch / sl-BEATs parquets, token tensors) used by the exploratory `analysis/` scripts |
| *raw_audio* | *6.3 GB* | *271,380* | *official `Development_data.zip` from Zenodo (not stored here); auto-fetched + extracted to `data/raw_audio`* |

Embeddings are frozen-encoder outputs (Google **Perch**, **BirdMAE**, sl-BEATs) plus
hand-designed acoustic features (harmonic-comb, background-whitened spectrum); they carry no
model weights.

## Provenance & attribution

The audio and labels are **redistributed from the official challenge dataset** under its
CC BY 4.0 license; all embeddings/features here are derived from it:

> **BioDCASE 2026 Challenge: Cross-Domain Mosquito Species Classification.**
> Yuanbo Hou, Vanja Zdravkovic, Marianne Sinka, Yunpeng Li, Kathy Willis, Stephen Roberts.
> Zenodo, 2026. DOI [10.5281/zenodo.20478577](https://doi.org/10.5281/zenodo.20478577).
> Licensed **CC BY 4.0**.

Challenge task page: <https://biodcase.github.io/challenge2026/task5>. Challenge coordinators:
Yuanbo Hou, Vanja Zdravkovic, Marianne Sinka, Kathy Willis, Stephen Roberts (University of
Oxford); Yunpeng Li, Mark Plumbley (King's College London); Wenwu Wang (University of Surrey).

The underlying dataset: 9 mosquito species across 5 acquisition domains (location / device /
acoustic environment), 271,380 clips (~60.7 h), highly imbalanced. The metric is `BA_unseen`
— species-balanced accuracy on the domain each species is *not* trained on.

## Citation

If you use these caches, please cite the source dataset and the challenge papers:

```bibtex
@dataset{hou2026cdmsc_dataset,
  title     = {{BioDCASE} 2026 Challenge: Cross-Domain Mosquito Species Classification},
  author    = {Hou, Yuanbo and Zdravkovic, Vanja and Sinka, Marianne and
               Li, Yunpeng and Willis, Kathy and Roberts, Stephen},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.20478577},
  url       = {https://doi.org/10.5281/zenodo.20478577}
}

@article{hou2026biodcase_baseline,
  title   = {{BioDCASE} 2026 Challenge Baseline for Cross-Domain Mosquito
             Species Classification},
  author  = {Hou, Yuanbo and others},
  journal = {arXiv preprint arXiv:2603.20118},
  year    = {2026}
}

@inproceedings{hou2026drbiol,
  title     = {Learning Domain-Robust Bioacoustic Representations for Mosquito
               Species Classification with Contrastive Learning and Distribution Alignment},
  author    = {Hou, Yuanbo and others},
  booktitle = {ICASSP 2026 - IEEE International Conference on Acoustics, Speech
               and Signal Processing (ICASSP)},
  pages     = {15207--15211},
  year      = {2026},
  doi       = {10.1109/ICASSP55912.2026.11464393}
}

@inproceedings{hou2025mtrcnn,
  title     = {Sound-Based Recognition of Touch Gestures and Emotions for
               Enhanced Human-Robot Interaction},
  author    = {Hou, Yuanbo and Ren, Q. and Wang, Wenwu and Botteldooren, D.},
  booktitle = {ICASSP 2025 - IEEE International Conference on Acoustics, Speech
               and Signal Processing (ICASSP)},
  pages     = {1--5},
  year      = {2025},
  doi       = {10.1109/ICASSP49660.2025.10890031}
}
```

## License

**CC BY 4.0**, inherited from the source dataset. You may share and adapt with attribution to
the dataset authors above. Note the challenge rules forbid *external labelled mosquito data*
for official submissions — the derived features here are computed only from the official
dataset.