Published March 4, 2022 | Version 1.0.0
Dataset Open

Hainan gibbons (Nomascus hainanus) calls for passive acoustic monitoring

Creators

  • 1. Stellenbosch University; African Institute for Mathematical Sciences
  • 2. Centre for Research into Ecological and Environmental Modelling, University of St Andrews; Centre for Statistics in Ecology, the Environment, and Conservation, University of Cape Town
  • 3. Institute of Zoology, Zoological Society of London; Department of Biological Sciences, Northern Illinois University
  • 4. School of Biological Sciences, University of Utah
  • 5. Institute of Zoology, Zoological Society of London
  • 6. Department of Life Sciences, University of Roehampton
  • 7. Living Collections, Zoological Society of London
  • 8. Bawangling National Nature Reserve

Description

This dataset extends an existing one (10.5281/zenodo.3991714).

Data accompanying the paper: "Passive Acoustic Monitoring and Transfer Learning"

Please cite this dataset as:

Dufourq, Emmanuel and Batist, Carly and Foquet, Ruben and Durbach, Ian. (2022). Passive Acoustic Monitoring and Transfer Learning. BioRxiv doi: 

This dataset contains approximately 10 hours of audio that contained calls of the critically endangered Hainan gibbons (Nomascus hainanus). The audio data was collected in the Bawangling National Nature Reserve, Malawi using 8 Song Meter SM3 recorders. The sampling rate was set to 9,600Hz and the recordings were collected between March to August 2016.

The annotations files are in (.svl) format which is compatible with SonicVisualiser (https://www.sonicvisualiser.org/). Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''gibbon" (presence class) or "no-gibbon" (absence class) -- this dataset can be used to train a binary classification model.

The audio files are provided in "Audio.zip" and the manually verified annotation in "Annotations.zip".

Notes

ED is supported by a research chairship from the African Institute for Mathematical Sciences South Africa. This work was carried out with the aid of a grant from the International Development Research Centre, Ottawa, Canada, www.idrc.ca, and with financial support from the Government of Canada, provided through Global Affairs Canada (GAC), www.international.gc.ca. This work was supported by funding from Microsoft's AI for Earth program. We thank the Management Office of Bawangling National Nature Reserve for logistical assistance in the field. Fieldwork was funded by an Arcus Foundation grant to STT and a Wildlife Acoustics grant to JVB. We also thank the following rangers who contributed to data collection: Guang Wei, Zhong Zhao, Qing Lin, Jinbing Zhang, Zhicheng Zhang, Quanjin Li, Xiaoliang Fu, Zhengchong Zhou, Lubiao Huang, Zhengkun Ye, Zhenghai Zou, Jinqiang Wang, Wentao Han and Zengnan Xie.

Files

Annotations.zip

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