People living near the airport are experiencing many inconveniences due to frequent aircraft noise. For these people, the government uses the aircraft noise evaluation unit (e.g., Lden) to calculate the degree of annoyance and then compensate for aircraft noise. Aircraft noise evaluation unit should be calculated only by aircraft noise, but the reality is not so. This is because the aircraft noise monitor measures not only aircraft noise but also loud background noise. Therefore, in this paper, we propose a method of recognizing only the aircraft noise among the stored noise from the noise monitor to calculate accurate aircraft noise evaluation unit. The proposal uses convolutional neural network, one of the deep learning techniques. Our proposal purposes less than 1% false-positive (FP) or false-negative (FN) rate.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Convolutional Neural Network Approach for Aircraft Noise Detection


    Contributors:
    Pak, Ju-won (author) / Kim, Min-koo (author)


    Publication date :

    2019-02-01


    Size :

    210294 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Convolutional Neural Networks for Aircraft Noise Monitoring

    Heller, Nicholas / Anderson, Derek / Baker, Matt et al. | ArXiv | 2018

    Free access

    Aircraft Detection using Deep Convolutional Neural Network in Small Unmanned Aircraft Systems

    Hwang, Sunyou / Lee, Jaehyun / Shin, Heemin et al. | AIAA | 2018


    Aircraft Detection in Remote Sensing Images Based on Deep Convolutional Neural Network

    Li, Yibo / Zhang, Senyue / Zhao, Jingfei et al. | IEEE | 2017


    Aircraft Detection using Deep Convolutional Neural Network in Small Unmanned Aircraft Systems (AIAA 2018-2137)

    Hwang, Sunyou / Lee, Jaehyun / Shin, Heemin et al. | British Library Conference Proceedings | 2018


    Aircraft accessory online detection system and method based on improved convolutional neural network

    LIU XINGGANG / WANG HAIBO | European Patent Office | 2022

    Free access