Determining disturbance sounds in aircraft sound events using a CNN-based method

Abstract

In this paper, we propose a method to determine whether an aircraft sound event contains disturbance sounds through a combination of sound source recognition models developed using a convolutional neural network. First, considering road traffic noise as a disturbance sound, distinct recognition models for aircraft and road traffic noise were developed. Second, simulated signals in which aircraft noise and road traffic noise were superimposed with different signal-to-noise ratios were input into the two recognition models. We investigated the variations in the output of each recognition model with respect to the signal-to-noise ratio. Subsequently, we obtained the output of each model for the case in which disturbance sounds affected an aircraft sound event. Third, we measured the aircraft noise around an airport, and the aircraft sound events on which road traffic noise was superimposed were input into the recognition models to examine the proposed method. It was confirmed that the proposed method helps detect the disturbance noise when intermittent noise from passing vehicles is included in an aircraft sound event.