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- **Repository:** [https://github.com/DBD-research-group/GADME](https://github.com/DBD-research-group/GADME)
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- **Paper:** [GADME](https://arxiv.org/)
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- **Point of Contact:** [Lukas Rauch](mailto:[email protected])
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### Datasets
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We present the BirdSet benchmark that covers a comprehensive range of classification datasets in avian bioacoustics.
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##### Test_5s
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- Task: Multilabel ("ebird_code_multilabel")
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- Only soundscape data from Zenodo formatted acoording to the Kaggle evaluation scheme.
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- Each recording is segmented into 5-second intervals
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- This contains segments without any labels which results in a [0] vector.
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##### Test
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- We provide the full recording with the complete label set and specified bounding boxes.
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- This dataset excludes recordings that do not contain bird calls ("no_call").
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#### Metadata
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| | format datasets. | description |
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| quality | Value("string") | x |
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| recordist | Value("string") | x |
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```
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##### Example Metadata Train
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```python
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- **Repository:** [https://github.com/DBD-research-group/GADME](https://github.com/DBD-research-group/GADME)
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- **Paper:** [GADME](https://arxiv.org/)
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- **Point of Contact:** [Lukas Rauch](mailto:[email protected])
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### Datasets
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We present the BirdSet benchmark that covers a comprehensive range of classification datasets in avian bioacoustics.
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##### Test_5s
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- Task: Multilabel ("ebird_code_multilabel")
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- Only soundscape data from Zenodo formatted acoording to the Kaggle evaluation scheme.
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- Each recording is segmented into 5-second intervals where each ground truth bird vocalization is assigned to.
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- This contains segments without any labels which results in a [0] vector.
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##### Test
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- We provide the full recording with the complete label set and specified bounding boxes.
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- This dataset excludes recordings that do not contain bird calls ("no_call").
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### Quick Use
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- For multi-label evaluation with a segment-based evaluation use the test_5s column for testing.
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- You could only load the first 5 seconds or a given event per recording to quickly create a training dataset.
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- We recommend to start with HSN. It is a medium size dataset with a low number of overlaps within a segment
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#### Metadata
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| | format datasets. | description |
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| quality | Value("string") | x |
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| recordist | Value("string") | x |
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##### Example Metadata Train
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```python
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