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Datasets:
DBD-research-group
/
BirdSet

Tasks:
Audio Classification
ArXiv:
Tags:
audio classification
multi-label classification
bird sounds
passive acoustic monitoring
DOI:
License:
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@@ -42,7 +42,7 @@ We have a complementary code base: https://github.com/DBD-research-group/BirdSet
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  [9]: https://xeno-canto.org/
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  [10]: https://xeno-canto.org
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- - We assemble a training dataset for each test dataset that is a subset of a complete XC snapshot. We extract all recordings that have vocalizations of the bird species appearing in the test dataset.
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  - We use the .ogg format for every recording and a sampling rate of 32 kHz.
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  - Each sample in the training dataset is a recording may have more than one vocalization of the corresponding bird species.
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  - Each recording in the training datasets has a unique recordist and the corresponding license from XC. We omit all recordings from XC that are CC-ND.
@@ -50,7 +50,7 @@ We have a complementary code base: https://github.com/DBD-research-group/BirdSet
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  - Snapshot date of XC: 03/10/2024
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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 the corresponding test soundscape files.
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  - We transform the scientific names of the birds into the corresponding ebird_code label.
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  - We offer detected events and corresponding cluster assignments to identify bird sounds in each recording.
@@ -192,6 +192,6 @@ We have a complementary code base: https://github.com/DBD-research-group/BirdSet
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  archivePrefix = {arXiv},
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  }
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- Note that each test in GADME dataset has its own citation. Please see the source to see
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  the correct citation for each contained dataset. Each file in the training dataset also has its own recordist. The licenses can be found in the metadata.
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  ```
 
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  [9]: https://xeno-canto.org/
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  [10]: https://xeno-canto.org
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+ - We assemble a training dataset for each test dataset that is a subset of a complete Xeno-Canto (XC) snapshot. We extract all recordings that have vocalizations of the bird species appearing in the test dataset.
46
  - We use the .ogg format for every recording and a sampling rate of 32 kHz.
47
  - Each sample in the training dataset is a recording may have more than one vocalization of the corresponding bird species.
48
  - Each recording in the training datasets has a unique recordist and the corresponding license from XC. We omit all recordings from XC that are CC-ND.
 
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  - Snapshot date of XC: 03/10/2024
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  **Train**
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+ - Exclusively using focal audio data from XC with quality ratings A, B, C and excluding all recordings that are CC-ND.
54
  - Each dataset is tailored for specific target species identified in the corresponding test soundscape files.
55
  - We transform the scientific names of the birds into the corresponding ebird_code label.
56
  - We offer detected events and corresponding cluster assignments to identify bird sounds in each recording.
 
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  archivePrefix = {arXiv},
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  }
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+ Note that each test in BirdSet dataset has its own citation. Please see the source to see
196
  the correct citation for each contained dataset. Each file in the training dataset also has its own recordist. The licenses can be found in the metadata.
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  ```