Electric bus interior sound quality prediction by combining time domain analysis and machine learning  

作　　者：ZHANG Enlai CHEN Yi ZHONGLIAN Ruoyu 

机构地区：[1]School of Mechanical and Automotive Engineering,Xiamen University of Technology,Xiamen 361024 [2]Xiamen Key Laboratory of Robot Systems and Digital Manufacturing,Xiamen University of Technology,Xiamen 361024

出　　处：《Chinese Journal of Acoustics》2025年第1期84-101,共18页声学学报(英文版)

基　　金：supported by the National Natural Science Foundation of China(12004136);the Natural Science Foundation of Fujian Province(2023J011438);the Major Educational Research Project of Fujian Province(FBJY20230154);the Key Research and Industrialization Project of Technological Innovation in Fujian Province(2023G013);the Science and Technology Project for High-level Talents of XMUT(YKJ22017R).

摘　　要：Aiming at the problem of sound quality feature modeling,a sound quality prediction method combining time domain analysis and machine learning is proposed.Firstly,the sample entropy is introduced,and the time-domain features of noise signals are extracted by combining the grey wolf optimization(GWO)and variable mode decomposition(VMD)to construct an objective parameter of sound quality.Secondly,in order to improve the vehicle interior sound quality mapping accuracy based on the extreme gradient boosting(XGBoost)algorithm,the adaptive weight(AW)and adaptive factor(AF)for particle swarm optimization(PSO)algorithm are improved,and sound quality modeling methods based on AW-PSO-XGBoost,AF-PSO-XGBoost and AWF-PSO-XGBoost are proposed.Ultimately,the training and testing results of 64 sets of electric bus interior sound quality data indicate that the determined AWF-PSO-XGBoost model has the best acoustic comfort prediction accuracy and fitting effect,with average relative error and consistency coefficient being 3.27%and 0.9889,respectively.

关 键 词：Electric bus Sound quality prediction Time domain analysis Machine learning 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]