摘要
针对传统乘用车舱内噪声感知烦恼度量化模型精度低的问题,提出了一种利用混合算法优化的神经网络模型预测舱内噪声感知烦恼度的评价方法。此混合算法融合麻雀搜索算法(SSA)和遗传算法(GA),对反向传播(BP)神经网络进行优化,根据声品质主客观评价数据,建立SSA-GA-BP网络的乘用车舱内噪声感知烦恼度客观量化模型,与BP模型、GA-BP模型、SSA-BP模型进行对比分析。结果表明,SSA-GA-BP模型能够实现更高的预测精度,更接近主观评价数值,泛化能力更强,可替代传统的声品质主观评价实验。
Addressing the issue of low accuracy in traditional quantitative models for the perception of annoyance due to noise in vehicles,an evaluation method is proposed using a neural network model optimized by a hybrid algorithm to predict the perceived annoyance of interior noise.This hybrid algorithm integrates the sparrow search algorithm(SSA)and genetic algorithm(GA)to optimize the back propagation(BP)neural network.Based on the subjective and objective sound quality evaluation data,an objective quantification model of interior noise annoyance using the SSA-GA-BP network is established and compared with BP,GA-BP,and SSA-BP models.The results show that the SSA-GA-BP model achieves higher prediction accuracy,closer to subjective evaluation figures,and stronger generalization capabilities,making it a viable alternative to traditional subjective experiments in sound quality evaluation.
作者
钱堃
谭璟
沈政华
厉濠阳
刘珂
王言夫
赵剑
QIAN Kun;TAN Jing;SHEN Zhenghua;LI Haoyang;LIU Ke;WANG Yanfu;ZHAO Jian(School of Mechanical Engineering,Dalian University of Technology,Dalian 116024)
出处
《声学学报》
EI
CAS
CSCD
北大核心
2024年第2期254-262,共9页
Acta Acustica
基金
中央高校基本科研业务费专项资金项目(DUT22RC(3)002)
中国博士后科学基金项目(2019M650657)资助。
关键词
舱内噪声
声品质
神经网络
遗传算法
麻雀搜索算法
Cabin noise
Sound quality
Neural network
Genetic algorithm
Sparrow search algorithm