HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset
This provides a new dataset for researchers studying malaria and bioacoustic detection, but it is incremental as it adds to existing audio datasets without introducing novel methods.
The authors tackled the problem of malaria vector monitoring by releasing a crowd-sourced acoustic mosquito dataset with 195,434 labels, of which about 10% are mosquito events, and demonstrated its utility by training a convolutional neural network on log-Mel features.
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.