Info: Zenodo’s user support line is staffed on regular business days between Dec 23 and Jan 5. Response times may be slightly longer than normal.

Published June 1, 2022 | Version v1
Dataset Open

DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Evaluation Set

  • 1. Queen Mary university of London
  • 2. University of Konstanz & Max Planck Institute of Animal Behavior
  • 3. Biotopia, Naturkundemuseum Bayern
  • 4. AGH University of Science and Technology,
  • 5. University of Oxford
  • 6. Queen Mary University of London
  • 7. Syracuse University
  • 8. University of Salford
  • 9. University of Surrey
  • 10. La Salle, Universitat Ramon Llull
  • 11. Centre National de la Recherche Scientifique (CNRS)
  • 12. Tilburg University & Naturalis Biodiversity Centre

Description

General Description

The evaluation set for task 5 of DCASE 2022 "Few-shot Bioacoustic Event Detection" consists of 46 audio files acquired from different bioacoustic sources. 

The first 5 annotations are provided for each file, with events marked as positive (POS) for the class of interest. 

This dataset is to be used for evaluation purposes during the task

Folder Structure

Evaluation_Set.zip

    |___DC/

        |____*.wav

        |____*.csv

    |___CT/

        |____*.wav

        |____*.csv

    |___CHE/

        |____*.wav

        |____*.csv

    |___MGE/

        |____*.wav

        |____*.csv

    |___MS/

        |____*.wav

        |____*.csv

    |___QU/

        |____*.wav

        |____*.csv

 

Evaluation_Set_5shots.zip has the same structure but contains only the *.wav files.

Evaluation_Set_5shots_annotations_only.zip has the same structure but contains only the *.csv files

The subfolders denote different recording sources and there may or may not be overlap between classes of interest from different wav files.

Annotation structure

Each line of the annotation csv represents an event in the audio file. The column descriptions are as follows:
[ Audiofilename, Starttime, Endtime, Q ]

Development Set

The development set for the same task can be found at: https://doi.org/10.5281/zenodo.6012309

Open Access

This dataset is available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
 

Contact info

Please send any feedback or questions to:
Ines Nolasco: i.dealmeidanolasco@qmul.ac.uk

Files

eval_README .md

Files (2.8 GB)

Name Size Download all
md5:b5234558cd4a2e1b974e3288a82a7226
3.0 kB Preview Download
md5:5212c0e133874bba1ee25c81ced0de99
2.8 GB Preview Download
md5:2183038133f5ae7ce411e8a85ced666e
14.7 kB Preview Download