Research output per year
Research output per year
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".
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".
| Date made available | 2022 |
|---|---|
| Publisher | Zenodo |
Research output
- 1 Article
-
Passive acoustic monitoring of animal populations with transfer learning
Dufourq, E., Batist, C., Foquet, R. & Durbach, I., 1 Sept 2022, In: Ecological Informatics. 70, 12 p., 101688.Research output: Contribution to journal › Article › peer-review
Open AccessFile37 Citations (Scopus)17 Downloads (Pure)