DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Development Set v3
Ines Nolasco and Shubhr Singh and Ariana Strandburg-Peshkin and Lisa Gill and Hanna Pamula and Joe Morford and Michael Emmerson and Frants Jensen and Helen Whitehead and Ivan Kiskin and Ester Vidana-Vila and Vincent Lostanlen and Veronica Morfi and Dan Stowell

@article{,
title= {DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Development Set v3},
journal= {},
author= {Ines Nolasco and Shubhr Singh and Ariana Strandburg-Peshkin and Lisa Gill and Hanna Pamula and Joe Morford and Michael Emmerson and Frants Jensen and Helen Whitehead and Ivan Kiskin and Ester Vidana-Vila and Vincent Lostanlen and Veronica Morfi and Dan Stowell},
year= {},
url= {https://dcase.community/challenge2022/task-few-shot-bioacoustic-event-detection},
abstract= {The development set for task 5 of DCASE 2022 Few-shot Bioacoustic Event Detection consists of 192 audio files acquired from different bioacoustic sources. The dataset is split into training and validation sets. Multi-class annotations are provided for the training set with positive (POS), negative (NEG) and unknown (UNK) values for each class. UNK indicates uncertainty about a class. Single-class (class of interest) annotations are provided for the validation set, with events marked as positive (POS) or unkwown (UNK) provided for the class of interest.

Also available at 
https://zenodo.org/record/6482837},
keywords= {bird, few-shot, bioacoustics, acoustics, animal, DCASE, event detection, mammal},
terms= {},
license= {http://creativecommons.org/licenses/by/4.0/},
superseded= {}
}