Datasets:
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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.
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