Context The database contains wav recordings from the same optical sensor inserted in-turn into six insectary boxes containing only one mosquito species of both sexes (about 200-300 flying mosquitoes in each cage). As the mosquitoes fly randomly through the sensor their wingbeat partially occludes the light from the transmitter to the receiver. The light fluctuation recorded is modulated by the wingbeat of the insect. The resulting signal is pseudo-acoustic, meaning that it sounds exactly like a microphone recording but has been acquired using optical means (however, not vision based). Insect Biometrics, in the context of our work, is a measurable behavioral characteristic of flying insects. Biometric identifiers are related to the shape of the body (main body size, wing shape, wingbeat frequency, pattern movement of the wings). Biometric identification methods use biometric characteristics or traits to verify species/sex identities when insects access endpoint traps following a bait.

Content • 279,566 wingbeat recordings correctly labeled

• 6 mosquito species (Ae. aegypti, Ae. albopictus, An. arabiensis, An. gambiae, Cu. pipiens, Cu. quinquefasciatus)

• 3 genera of mosquito species (Aedes, Anopheles, Culex)

Acknowledgements The data have been recorded at the premises of Biogents, Regensburg, Germany (https://www.biogents.com/) and with the help of IRIDEON SA, Spain (http://irideon.eu/ ). The data have been recorded using the device published in:

Potamitis I. and Rigakis I., "Large Aperture Optoelectronic Devices to Record and Time-Stamp Insects’ Wingbeats," in IEEE Sensors Journal, vol. 16, no. 15, pp. 6053-6061, Aug.1, 2016. doi: 10.1109/JSEN.2016.2574762

The REMOSIS project that supported the creation of the database has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 691131.

We gratefully acknowledge the support of NVIDIA Corporation with the donation of a TITAN-X GPU used for training the deep learning networks used to classify mosquitoes’ spectra.

Inspiration The point of having such recordings is to eventually embed optoelectronic sensors in automatic traps that will report counts, species and sex identity of captured mosquitoes. All species of this dataset can be dangerous as they are potential vectors of pathogens that cause serious illnesses. A widespread network of traps for insects of economic importance such as fruit flies and of hygienic importance such as mosquitoes allows the automatic creation of spatiotemporal maps and cuts down significantly the manual cost of visiting the traps. The creation of historical data can lead to the prediction of outbreaks and risk assessment in general.

We provide code to read the data and extract the power spectral density signature of each wingbeat. We also extract Mel-scaled, filter-bank features. How about wavelets and time-varying autoregressive models? The starter code using top-tier shallow classifiers achieves a mean accuracy of 81-84%. Deep-learning performs better. Can you classify genus, perform clustering, apply transfer learning to spectral data?

Come aboard and help humanity against killer mosquitoes!