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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:lukas.rauch@uni-kassel.de)
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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 without overlaps.
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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:lukas.rauch@uni-kassel.de)
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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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+ -
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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