Detection and classification of insect sounds in a grain silo using a neural network

This paper presents the application of a time-delay neural network to the detection and classification of time signatures produced by insect sounds in a stored grain silo. Conventional methods of insect monitoring can only detect some of the adult insects and none of the larvae insects, which are the most destructive to the grain. The acoustic vibrations generated by the adult and larvae when moving or chewing have distinct time signatures. Random grain settling vibrations and external vibrations add noise to the system. A time-delay neural network with feature extraction was successfully trained to distinguish between these four classes of sounds.