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- config_name: UHH_noise
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path: UHH/UHH_polyphonic/train-*
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path: UHH/UHH_polyphonic/validation-*
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path: UHH/UHH_polyphonic/test-*
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path: UHH/UHH_segments/validation-*
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path: UHH/UHH_segments/test-*
- config_name: UHH_soundscape_test
data_files:
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path: UHH/UHH_soundscape_test/test_5s-*
- config_name: XCM_polyphonic
data_files:
- split: train
path: XCM/XCM_polyphonic/train-*
- split: validation
path: XCM/XCM_polyphonic/validation-*
- split: test
path: XCM/XCM_polyphonic/test-*
- config_name: XCM_segments
data_files:
- split: train
path: XCM/XCM_segments/train-*
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path: XCM/XCM_segments/validation-*
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path: XCM/XCM_segments/test-*
license: cc-by-nc-4.0
PolyBirdMix
- Point of Contact: m.cohrt@campus.tu-berlin.de
Dataset Summary
PolyBirdMix is a corpus of synthetic soundscape mixtures generated from individual bird call recordings sourced from BirdSet. Individual recordings are randomly mixed to produce a large collection of polyphonic soundscapes covering varying degrees of polyphony and signal-to-noise ratios. This enables controlled model training and systematic evaluation for tasks such as polyphony estimation, source separation, denoising, and multi-label classification.
The dataset is organized by geographic subset, mirroring the subset structure of BirdSet (e.g. HSN, PER, ...). Additional subsets will be added over time.
Dataset Structure
Data Instances
Each instance in the dataset represents a synthetic audio mixture along with associated metadata describing its composition. Additionally it contains the same mixture without background noise and the source files of the mix.
Data Fields
mixtures:
audio: Audio mixture fileno_noise_audio: Audio mixture file without noisesource_{index}_audio: Audio segments used as sources for mixturenoise_audio: Noise audio segmentpolyphony_degree: Number of simultaneously vocalizing birdsbirdset_id_multilabel: List of species present in the mixturesnr_dB: Signal-to-noise ratio of the mixture in dBraw_files_*: Metadata of raw files
noise:
audio: Background noise recording
Subsets
| Subset | Region | Status |
|---|---|---|
| PER | (Amazon Basin) | Available |
| NES | (Colombia, Costa Rica) | Available |
| UHH | (Hawaiian Islands) | Available |
| HSN | (High Sierras) | Available |
| POW | (Powdermill Nature) | Available |
| SSW | (Sapsucker Woods) | Available |
| SNE | (Sierra Nevada) | Available |
| (XCM) | ( Xenocanto Subset M) | Available |
Subset identifiers follow the BirdSet naming convention. Refer to the BirdSet dataset card for detailed descriptions of each geographic region.
Dataset configs
Each subset (e.g. HSN) contains the following configs:
| Config | Description |
|---|---|
{subset}_polyphonic |
Synthetic mixtures of single species segments |
{subset}_segments |
Single species segments used during mixing |
{subset}_noise |
Background noise recordings used during mixing |
{subset}_soundscape_test |
Soundscape recordings for testing |
Dataset Creation
Source Data
Individual bird call recordings are sourced from BirdSet, which itself aggregates recordings from Xeno-canto and other sources. Please refer to the BirdSet dataset card for detailed licensing and provenance of source recordings.
Curation Rationale
Synthetic mixtures are used to enable precise control over polyphony level and SNR, which is not possible with naturally recorded soundscapes. This allows systematic evaluation of model performance across a range of acoustic conditions.
Known Limitations
- Mixtures are synthetic — real-world soundscapes may exhibit different acoustic properties and overlap patterns not captured here.
- Source recordings are subject to the biases and coverage gaps present in BirdSet and Xeno-canto (e.g. geographic and species imbalances).
Credits
This project builds on the following open-source work:
- BirdSet — Bird call audio data
- Perch — BirdMixIT source separation model
- BirdnetLib - Python api for BirdNET-Analyzer
- BirdNET Analyzer - Bird call analyzer
- TU Studio HPC Cluster ML Workflow — SLURM pipeline architecture
Citations
BirdSet: Rauch et al., 2024. BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics. arXiv:2403.10380.
BibTeX
bibtex@misc{rauch2024birdset, title={BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics}, author={Lukas Rauch et al.}, year={2024}, eprint={2403.10380}, archivePrefix={arXiv}, url={https://arxiv.org/abs/2403.10380}, }eBird Taxonomy: Clements et al., 2024. The eBird/Clements Checklist of Birds of the World: v2024. Cornell Lab of Ornithology.
BibTeX
bibtex@misc{clements2024ebird, title={The eBird/Clements Checklist of Birds of the World: v2024}, author={Clements, J. F. and Rasmussen, P. C. and Schulenberg, T. S. and Iliff, M. J. and Fredericks, T. A. and Gerbracht, J. A. and Lepage, D. and Spencer, A. and Billerman, S. M. and Sullivan, B. L. and Smith, M. and Wood, C. L.}, year={2024}, howpublished={\url{https://www.birds.cornell.edu/clementschecklist/download/}} }Bird-MixIT: Denton et al., 2021. Improving Bird Classification with Unsupervised Sound Separation. arXiv:2110.03209.
BibTeX
bibtex@misc{denton2021improvingbirdclassificationunsupervised, title={Improving Bird Classification with Unsupervised Sound Separation}, author={Tom Denton and Scott Wisdom and John R. Hershey}, year={2021}, eprint={2110.03209}, archivePrefix={arXiv}, primaryClass={eess.AS}, url={https://arxiv.org/abs/2110.03209}, }MixIT: Wisdom et al., 2020. Unsupervised Sound Separation Using Mixture Invariant Training. arXiv:2006.12701.
BibTeX
bibtex@misc{wisdom2020unsupervisedsoundseparationusing, title={Unsupervised Sound Separation Using Mixture Invariant Training}, author={Scott Wisdom and Efthymios Tzinis and Hakan Erdogan and Ron J. Weiss and Kevin Wilson and John R. Hershey}, year={2020}, eprint={2006.12701}, archivePrefix={arXiv}, primaryClass={eess.AS}, url={https://arxiv.org/abs/2006.12701}, }BirdNET: Kahl et al., 2021. BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61.
BibTeX
bibtex@article{kahl2021birdnet, title={BirdNET: A deep learning solution for avian diversity monitoring}, author={Kahl, Stefan and Wood, Connor M and Eibl, Maximilian and Klinck, Holger}, journal={Ecological Informatics}, volume={61}, pages={101236}, year={2021}, publisher={Elsevier} }License
This dataset is released under CC BY-NC 4.0. Source recordings inherit the licensing terms of BirdSet — please consult the BirdSet dataset card for details.