A ResNet-9 Model for Insect Wingbeat Sound Classification

Béla J. Szekeres, Natabara Máté Gyöngyössy, János Botzheim

Abstract: Sound-based insect wingbeat classification presents a unique challenge with implications for areas such as mosquito control and the prevention of mosquito-borne diseases. This paper introduces a straightforward modified ResNet-9 model to address this challenge by utilizing one-dimensional convolutional layers. The architecture of the proposed ResNet-9 model is outlined in detail. Impressively, the model can accurately classify fruitflies and mosquitoes using raw audio data instead of relying on spectrograms. Its performance surpasses the majority of preceding models while concurrently reducing the number of trainable parameters by 90%. The results from this research carry notable significance for practical applications in insect control and disease prevention.