Update README.md

README.md

@@ -8,7 +8,7 @@ tags:
 ---
 # Dataset Description
 
-- **Repository:** [https://github.com/DBD-research-group/GADME](https://github.com/DBD-research-group/BirdSet)
+- **Repository:** [https://github.com/DBD-research-group/BirdSet](https://github.com/DBD-research-group/BirdSet)
 - **Paper:** [BirdSet](https://arxiv.org/abs/2403.10380)
 - **Point of Contact:** [Lukas Rauch](mailto:lukas.rauch@uni-kassel.de)
 
@@ -16,7 +16,7 @@ tags:
 Deep learning models have emerged as a powerful tool in avian bioacoustics to assess environmental health. To maximize the potential of cost-effective and minimal-invasive 
 passive acoustic monitoring (PAM), models must analyze bird vocalizations across a wide range of species and environmental conditions. However, data fragmentation 
 challenges a evaluation of generalization performance. Therefore, we introduce the BirdSet dataset, comprising approximately 520,000 global bird recordings 
-for training and over 400 hours PAM recordings for testing.
+for training and over 400 hours PAM recordings for testing in multi-label classification.
 - **Complementary Code**:[https://github.com/DBD-research-group/GADME](https://github.com/DBD-research-group/BirdSet)
 - **Complementary Paper**: [https://arxiv.org/abs/2403.10380](https://arxiv.org/abs/2403.10380)
 
@@ -46,7 +46,7 @@ for training and over 400 hours PAM recordings for testing.
 [9]:  https://xeno-canto.org/
 [10]: https://xeno-canto.org
 
-- 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.
+- 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.
 - The focal training datasets or soundscape test datasets components can be individually accessed using the identifiers **NAME_xc** and **NAME_scape**, respectively (e.g., **HSN_xc** for the focal part and **HSN_scape** for the soundscape).
 - We use the .ogg format for every recording and a sampling rate of 32 kHz.
 - Each sample in the training dataset is a recording that may contain more than one vocalization of the corresponding bird species.
@@ -64,13 +64,12 @@ Each dataset (except for XCM and XCL that only feature Train) comes with a datas
 - We provide the full recordings from XC. These can generate multiple samples from a single instance.
 
 **Test_5s**
-- Task: Multilabel ("ebird_code_multilabel")
+- Task: Adapted to multilabel classification ("ebird_code_multilabel")
 - Only soundscape data from Zenodo formatted acoording to the Kaggle evaluation scheme.
 - Each recording is segmented into 5-second intervals where each ground truth bird vocalization is assigned to. 
 - This contains segments without any labels which results in a [0] vector.
 
 **Test**
-- Task: Multiclass ("ebird_code")
 - 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").