Identification of jet aircraft model based on frequency characteristics of noise by convolutional neural network

Abstract

We have been developing an aircraft model identification system that uses a convolutional neural network (CNN). The assumption is that this identification system would be used to estimate the number of flights to create noise maps. In our previous study, we used the CNN model to classify five aircraft comprising three rotorcraft, one turboprop, and one jet aircraft, and the accuracy reached 99%. In the present study, to examine whether this method is also effective for identifying the sound sources of jet aircraft, we conducted two case studies using frequency characteristics of aircraft noise obtained from field measurements around Osaka International Airport and Narita International Airport. Targeting 7 and 18 types of sound source at Osaka and Narita, respectively, an identification rate of 98% was obtained in both cases. This suggests that the present system can estimate the number of jet aircraft flights for each engine type or each aircraft model with very high accuracy.