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 6 hours of audio that contained calls of the pin-tailed whydah (Vidua macroura). The audio data was collected in the Intaka Island Nature Reserve in Cape Town, South Africa using 1 Audiomoth. The sampling rate was set to 48,000Hz and the recordings were obtained over four days in January 2021. 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.