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. 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. Files provided Audio.zip - contains audio files (.wav) Annotations.zip - contains the corresponding labels (.svl) for Sonic Visualiser Class description CRC: calls of the Cape robin-chat (Cossypha caffra) PTW: calls of the pin-tailed whydah (Vidua macroura) NOISE: any sound event that does not contain a Cape robin-chat or pin-tailed whydah call Parts of this data was used in two MSc dissertations: "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 "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

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.