| --- |
| 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. |
|
|
