Aircraft automatic detection on Station7hz interface using signal processing and artificial intelligence - ACOUDET - First method

Abstract

The global project is to design a automatic noise event detection and classification system in real time using signal processing and artificial intelligence (AI). The first phase focuses on transport noise (aircrafts, trains, road vehicles, etc.). Secondly, the plan is to extend detection to construction site noise and any natural or life sources. This paper presents the first method developed to detect audible aircrafts pass-by. It focused on the development of a prototype detection algorithm, its results in situ conditions, and insights on future developments to create a robust aircraft and train noise detection system. The data collection method, aircraft audio signal particularities and a first detection method are described. The robustness of the detections is evaluated and limitation induced by real time detection are discussed. Thanks to the proprietary monitoring system Station-7hz. it has been possible to detect aircrafts in different types of sound environments in a straightforward manner, showing better results at night due to a quieter environment. Future developments based on a hybrid algorithm include AI models for audibility assessment and unwanted event filtering during aircraft pass-by. Aircraft's presence is detected via radar data and the detection of maximum acoustic levels correlated with aircraft parameters.