[1]曾发林,蔡嘉伟,孙苏民.基于CEEMD的非稳态排气噪声声品质预测[J].郑州大学学报(工学版),2020,41(06):19-25.
 Sound quality prediction of unsteady exhaust noise based on CEEMD[J].Journal of Zhengzhou University (Engineering Science),2020,41(06):19-25.
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基于CEEMD的非稳态排气噪声声品质预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41
期数:
2020年06期
页码:
19-25
栏目:
出版日期:
2020-12-31

文章信息/Info

Title:
Sound quality prediction of unsteady exhaust noise based on CEEMD
作者:
曾发林蔡嘉伟孙苏民
文献标志码:
A
摘要:
为了预测汽车非稳态排气噪声声品质,采用Zwicker时变算法计算了样车加速排气噪声的心理声学客观参量值,并建立基于心理声学客观参量的GA-BP声品质预测模型.同时,运用互补总体经验模态分解(CEEMD)对非稳态排气噪声信号进行分解,得到多个IMF分量,并对IMF分量进行样本熵特征计算,为了减少冗余和过度拟合的可能性并尽可能地保留原始数据的主特征,采用主成分分析(PCA)对数据进行降维处理,得到新参量SQP-CSP(Sound quality parameter base on CEEMD and then proceed SE-PCA),并建立新的预测模型.结果表明,根据新参量建立的模型对非稳态信号声品质预测具有更高的精度,更能体现非稳态信号的特征
Abstract:
In order to predict the sound quality of automobile unsteady exhaust noise, Zwicker time varying method was applied to calculate the psychoacoustic objective parameter values in terms of exhaust noise of cars at accelerated velocityThereby a prediction model of GA-BP sound quality based on psychoacoustic objective parameters was established.Meanwhile,the Complementary Ensemble Empirical Mode Decomposition(CEEMD) was used to decompose the signals of the unsteady exhaust noise, obtaining multiple IMF components and calculating sample entropy of the IMF components. In order to reduce the possibility of redundancy and over-fitting, and retain the main features of the original data, Principal Component Analysis(PCA) was applied to reduce dimension of data, so as to establish a new sound quality parameter SQP-CSP(Sound quality parameter base on CEEMD and then proceed SE-PCA) ,establishing the new prediction model.The results indicate that the model based on the new parameter has higher precision for predicting the sound quality of unsteady signals, and it can better reflect the characteristics of unsteady signals.
更新日期/Last Update: 2021-02-10