Metro Neighborhood Construction Monitoring System Based on Neural Network

In order to avoid impact of illegal construction and wrong construction along the rail transit, it is necessary to build an environmental monitoring and alarm system. This study uses Mel Frequency Cepstrum Coefficient (MFCC) as the sound feature to build a neural network model to identify the constr...

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Bibliographic Details
Published in2021 IEEE International Conference on Emergency Science and Information Technology (ICESIT) pp. 473 - 478
Main Authors Rong, Lu, Yu, Anning, Wang, Bocheng, Xiong, Cong, Gao, Xinghua, Yuan, Senhao, Yang, Zhihan
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.11.2021
Subjects
Arduino MCU
Cepstrum
Conferences
LoRa ad hoc network
machine learning
neural network
Neural networks
Object recognition
Power demand
Rails
Real-time systems
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Summary:In order to avoid impact of illegal construction and wrong construction along the rail transit, it is necessary to build an environmental monitoring and alarm system. This study uses Mel Frequency Cepstrum Coefficient (MFCC) as the sound feature to build a neural network model to identify the construction sound to monitor whether the construction is in progress in the non construction area. Thanks to the long-distance transmission and low power consumption characteristics of Lora, the identification result is transmitted from the device node to the PC, so the administrator can view the status information of each device node in real time. At last, the accuracy of sound classification and recognition is as high as 99.6%.
DOI:10.1109/ICESIT53460.2021.9696888