Whispers in the air: Designing acoustic classifiers to detect fruit flies from afar

Detecting weak wingbeats of a flying bug is a challenging problem in uncontrolled outdoor settings. In this work, we show that proper treatment of environmental noise is a key factor in robust acoustic classifier design and propose a novel environmental noise treatment method. Our proposed method generalizes over different classifiers and features. Our algorithm provides robust detection and classification of multiple bugs, over longest ranges reported, using simple microphones. In order to benchmark research in this area, we release a novel dataset containing acoustic data of four bugs (Guava fly, Melon fly, Blue bottle fly, and mosquitoes). We additionally investigate the feasibility of deploying our acoustic classifier on a noisy mobile platform, i.e., a drone. To this end, we expose the limitations of signal processing techniques to deal with loud drone noise. We demonstrate how soundproofing can be used to design acoustic sensing for drones. • Non-contact detection and classification of common flying bugs (e.g., 10 fruit flies) from afar. • Noise treatment for acoustic classifiers for detection of feeble wingbeat signals in presence of large environmental noise. • Deployment in real-world context with actual drone noise.