Omkar2118/datasets huggingface dataset:
BirdCLEF 2019
Full name: BirdCLEF 2019

BirdClef 2019 is a bird soundscape dataset. It contains around 350 hours of manually annotated soundscapes using 30 field recorders between January and June of 2017 in Ithaca, NY, USA. There are around 50,000 recordings in the dataset in total, with 659 classes. The dataset also contains species tags.

Source: [https://www.imageclef.org/BirdCLEF2019](https://www.imageclef.org/BirdCLEF2019)
Image Source: [http://dcase.community/challenge2018/task-bird-audio-detection](http://dcase.community/challenge2018/task-bird-audio-detection)
BirdCLEF 2018
Full name: BirdCLEF 2018

BirdClef 2018 is a bird soundscape dataset based on the contributions of the Xeno-canto network. The training set contains 36,496 recordings covering 1500 species of central and south America (the largest bioacoustic dataset in the literature). There are about 68 hours of recordings in total, with 1,500 classes and species tags.

Source: [https://www.imageclef.org/BirdCLEF2019](https://www.imageclef.org/BirdCLEF2019)
Image Source: [http://dcase.community/challenge2018/task-bird-audio-detection](http://dcase.community/challenge2018/task-bird-audio-detection)
DESED
Full name: Domestic environment sound event detection

The **DESED** dataset is a dataset designed to recognize sound event classes in domestic environments. The dataset is designed to be used for sound event detection (SED, recognize events with their time boundaries) but it can also be used for sound event tagging (SET, indicate presence of an event in an audio file).
The dataset is composed of 10 event classes to recognize in 10 second audio files. The classes are: Alarm/bell/ringing, Blender, Cat, Dog, Dishes,
Electric shaver/toothbrush, Frying, Running water, Speech, Vacuum cleaner.

Source: [https://project.inria.fr/desed/](https://project.inria.fr/desed/)
Image Source: [https://project.inria.fr/desed/](https://project.inria.fr/desed/)

Introducing paper: Sound event detection in domestic environments withweakly labeled data and soundscape synthesis