5141676
doi
10.5281/zenodo.5141676
oai:zenodo.org:5141676
Dufourq, Emmanuel
Stellenbosch University; African Institute for Mathematical Sciences
Tamanjong, Mikwa Boris
University of Rwanda
Nshimiyimana, Aime
University of Rwanda
Pin-tailed whydah and Cape robin-chat calls for passive acoustic monitoring
Dufourq, Emmanuel
Stellenbosch University; African Institute for Mathematical Sciences
info:eu-repo/semantics/openAccess
Creative Commons Attribution Non Commercial Share Alike 4.0 International
https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
Cape robin-chat, pin-tailed whydah, vocalisation classification, passive acoustic monitoring, bioacoustics
<p>We provide the audio data (.wav) used to train neural network classifiers along with the corresponding labelled files (.svl). The .svl files are natively read using Sonic Visualiser (https://www.sonicvisualiser.org/) but can directly be read using Python as these are XML files.</p>
<p>This is a three class classification dataset. The recordings were obtained using an AudioMoth which was placed at one location in Intaka Island Nature Reserve, Cape Town, South Africa. The recorder was attached to a tree at approximately 1.5 meters from the ground. The sampling rate was set to 48000Hz with a bit rate of 768kbps. The recordings took place in January 2021. While further recordings exist we only provide a small subset here. Additional data can be requested.</p>
<p><strong>Files provided</strong></p>
<p>Audio.zip - contains audio files (.wav)</p>
<p>Annotations.zip - contains the corresponding labels (.svl) for Sonic Visualiser</p>
<p><strong>Class description</strong></p>
<p>CRC: calls of the Cape robin-chat (Cossypha caffra)</p>
<p>PTW: calls of the pin-tailed whydah (Vidua macroura)</p>
<p>NOISE: any sound event that does not contain a Cape robin-chat or pin-tailed whydah call</p>
<p> </p>
<p>Parts of this data was used in two MSc dissertations:</p>
<ul>
<li>"Acoustic Data Augmentation for Small Passive Acoustic Monitoring Datasets", Aime Nshimiyimana, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li>
<li>"Pre-training neural networks on Xeno-Canto and eBird for bioacoustic classification models", Mikwa Boris Tamanjong, African Centre of Excellence in Data Science (ACE-DS) of the University of Rwanda, College of Business and Economics</li>
</ul>
We thank Intaka Island Nature Reserve, Cape Town, South Africa for providing access and allowing us to record. We also thank Ian Durbach and Mark Heerden for funding the various audio equipment.
Zenodo
2021-08-03
info:eu-repo/semantics/other
5141675
1.0.0
1627998501.221274
1355408440
md5:6762b6747cb68fcd5be4680fef3e3477
https://zenodo.org/records/5141676/files/Audio.zip
25955
md5:832f865cbf77dfda15cd151e489032d3
https://zenodo.org/records/5141676/files/Annotations.zip
public
10.5281/zenodo.5141675
isVersionOf
doi