Honey Bee Piping Detection Utilizing Convolutional Neural Networks

Audio recordings from inside honey beehives can provide significant information about the health and build-up status of the hives. Bees emit important bioacoustic indicators, such as auditory piping signals, which can play a significant role in determining the growth and health status of the hive. As part of the Appalachian Multipurpose Apiary Informatics System (AppMAIS) project, we have installed microphones inside 28 beehives in the Western region of North Carolina. Several of these hives have swarmed over the past two years, allowing us to collect a multitude of recordings containing piping. Using this data, we have trained a piping detector convolutional neural network (CNN), which can function with high accuracy across three unique hives. This article provides details regarding the acoustic properties of the relevant subclasses of piping signals and the design and implementation of a piping classifier.