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 vulnerable Thyolo Alethe (Chamaetylas choloensis). The audio data was collected in the Mount Mulanje Biosphere Reserve, Malawi using 10 Audiomoths. The sampling rate was set to 32,000Hz and the recordings were obtained over five days in November 2020. A larger dataset exists. 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 ''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 "Audio.zip" and the manually verified annotation in "Annotations.zip".

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. The audio data containing calls of the Thyolo Alethe was collected by an international team of researchers from the Biodiversity Inventory for Conservation (BINCO) and the Wildlife and Environmental Society of Malawi (WESM), led by Ruben Foquet (BINCO) and Dr. Tiwonge Gawa (WESM and the Malawi University of Science and Technology, MUST).