  Mosquitoes are the only known vector of malaria, which leads to hundreds of
thousands of deaths each year. Understanding the number and location of
potential mosquito vectors is of paramount importance to aid the reduction of
malaria transmission cases. In recent years, deep learning has become widely
used for bioacoustic classification tasks. In order to enable further research
applications in this field, we release a new dataset of mosquito audio
recordings. With over a thousand contributors, we obtained 195,434 labels of
two second duration, of which approximately 10 percent signify mosquito events.
We present an example use of the dataset, in which we train a convolutional
neural network on log-Mel features, showcasing the information content of the
labels. We hope this will become a vital resource for those researching all
aspects of malaria, and add to the existing audio datasets for bioacoustic
detection and signal processing.
