Update README.md

README.md

@@ -6,30 +6,37 @@ tags:
 - bird classification
 - passive acoustic monitoring
 ---
+## Dataset Description
+
+- **Repository:** [https://github.com/s3prl/s3prl](https://github.com/s3prl/s3prl)
+- **Paper:** [GADME](https://arxiv.org/))
+- **Point of Contact:** [Lukas Rauch](mailto:lukas.rauch@uni-kassel.de) and [Moritz Wirth](mailto:moritz.wirth@uni-kassel.de)
+
+### Dataset Summary
 We present the GADME benchmark that covers a comprehensive range of avian monitoring datasets. 
 We offer a static set of evaluation datasets and a varied collection of training datasets, enabling the application of diverse methodologies
 
 ### Datasets
     
-#### Train
+##### Train
 - Exclusively using focal audio data from Xeno-Canto (XC) with quality ratings A, B, C and excluding all recordings that are CC-ND.
 - Each dataset is tailored for specific target species identified in soundscape files.
 - We offer detected events and corresponding cluster assignments to identify bird sounds in each recording.
 - We provide the full recordings from XC! These can generate multiple samples from a single instance.
 
-#### Test
+##### Test
 - Only soundscape data sourced from Zenodo.
 - We provide the full recording with the complete label set and specified bounding boxes.
 - This dataset excludes recordings that do not contain bird calls ("no_call").
 - Task: Multiclass ("ebird_code")
 
-#### Test_5s
+##### Test_5s
 - Only soundscape data from Zenodo formatted acoording to the Kaggle evaluation scheme.
 - Each recording is segmented into 5-second intervals without overlaps.
 - This contains the "no_call" class.
 - Task: Multilabel ("ebird_code_multilabel")
 
-### Subsets
+#### Subsets
 
 Numbers need to be updated