Published March 6, 2022 | Version 1.0.0
Dataset Open

Black-and-white ruffed lemur (Varecia variegata) calls for passive acoustic monitoring

  • 1. Graduate Center of the City University of New York, New York City, NY, USA
  • 2. Centre ValBio, Ranomafana, Madagascar
  • 3. University of Antananarivo, Antananarivo, Madagascar
  • 4. Graduate Center of the City University of New York, New York City, NY, USA; Hunter College, City University of New York, New York City, NY, USA
  • 5. Stellenbosch University; African Institute for Mathematical Sciences


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 60 hours of audio that contained calls of the critically endangered Black-and-white ruffed lemur (Varecia variegata). The audio data was collected in a sub-humid rainforest site (Mangevo) in the southeast of Ranomafana National Park in Madagascar using 2 Swift recorders (Cornell Center for Conservation Bioacoustics). The sampling rate was set to 48,000Hz and the recordings were collected intermittently between May 2019 and November 2020. A larger dataset exists and further recordings will be released.

The annotations files are in (.svl) format which is compatible with SonicVisualiser ( Each audio file has a corresponding .svl file. Each .svl has segments of audio that were manually annotated as either ''thyolo-alethe" (presence class) or "noise" (absence class) -- this dataset can be used to train a binary classification model.

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


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,, and with financial support from the Government of Canada, provided through Global Affairs Canada (GAC), This work was supported by funding from Microsoft's AI for Earth program. The audio data was collected by Carly Batist.


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