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  - bird classification
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  - passive acoustic monitoring
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  ---
 
 
 
 
 
 
 
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  We present the GADME benchmark that covers a comprehensive range of avian monitoring datasets.
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  We offer a static set of evaluation datasets and a varied collection of training datasets, enabling the application of diverse methodologies
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  ### Datasets
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- #### Train
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  - Exclusively using focal audio data from Xeno-Canto (XC) with quality ratings A, B, C and excluding all recordings that are CC-ND.
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  - Each dataset is tailored for specific target species identified in soundscape files.
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  - We offer detected events and corresponding cluster assignments to identify bird sounds in each recording.
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  - We provide the full recordings from XC! These can generate multiple samples from a single instance.
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- #### Test
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  - Only soundscape data sourced from Zenodo.
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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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  - Task: Multiclass ("ebird_code")
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- #### Test_5s
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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 the "no_call" class.
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  - Task: Multilabel ("ebird_code_multilabel")
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- ### Subsets
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  Numbers need to be updated
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  - bird classification
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  - passive acoustic monitoring
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  ---
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+ ## Dataset Description
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+
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+ - **Repository:** [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl)
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+ - **Paper:** [GADME](https://arxiv.org/))
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+ - **Point of Contact:** [Lukas Rauch](mailto:lukas.rauch@uni-kassel.de) and [Moritz Wirth](mailto:moritz.wirth@uni-kassel.de)
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+
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+ ### Dataset Summary
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  We present the GADME benchmark that covers a comprehensive range of avian monitoring datasets.
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  We offer a static set of evaluation datasets and a varied collection of training datasets, enabling the application of diverse methodologies
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  ### Datasets
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+ ##### Train
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  - Exclusively using focal audio data from Xeno-Canto (XC) with quality ratings A, B, C and excluding all recordings that are CC-ND.
23
  - Each dataset is tailored for specific target species identified in soundscape files.
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  - We offer detected events and corresponding cluster assignments to identify bird sounds in each recording.
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  - We provide the full recordings from XC! These can generate multiple samples from a single instance.
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+ ##### Test
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  - Only soundscape data sourced from Zenodo.
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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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  - Task: Multiclass ("ebird_code")
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+ ##### Test_5s
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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 the "no_call" class.
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  - Task: Multilabel ("ebird_code_multilabel")
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+ #### Subsets
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  Numbers need to be updated
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