基于临界频带分量特征的柴油机辐射噪声声品质评价方法

Sound Quality Evaluation Method for Diesel Engine Radiated Noise Based on Critical Band Component Characteristics

摘要

现有的柴油机辐射噪声声品质评价一般是根据噪声样本总体特征进行评价，由于人的听觉系统具有频率选择性且还存在频率掩蔽效应，造成客观评价难以准确反映人的主观感受．本文提出了一种基于临界频带分量特征的柴油机辐射噪声声品质评价方法，该方法将噪声信号进行分解，得出一系列窄带本征模态函数(IMF)分量，并根据每一个IMF的中心频率将其映射到临界频带分析域中，结合主观特征描述，确定了对应于不同临界频带范围的5个显著影响主观感受的客观心理声学参数及其组合．最后，针对论文中的噪声样本集，使用多元线性回归、支持向量机、BP神经网络和 1D 卷积神经网络对本论文提出的方法进行了验证，与传统方法相比，模型预测值与评价值之间的R²值均有提高，最大增幅达到15.42%．验证结果显著证明了该方法的有效性，可为柴油机声品质评价领域的进一步研究和应用提供参考．

Abstract

Current methods for evaluating radiated noise quality in diesel engines typically assess the overall characteristics of noise samples. However，owing to the frequency selectivity and masking effects of the human auditory system，objective evaluations often do not accurately reflect subjective perceptions. This paper introduced a diesel engine noise quality evaluation method based on critical band component characteristics. The proposed method decomposed the noise signal into narrow-band intrinsic mode function(IMF)components，with each IMF mapped to a critical band analysis domain based on its central frequency. By integrating subjective descriptive features，five key psychoacoustic parameters and their combinations were identified across various critical band ranges with a pronounced impact on subjective perception. Finally，the proposed method was validated using the noise sample set from this study through multiple linear regression，support vector machine，BP neural network，and 1D convolutional neural network. Compared with traditional methods，the R² values are better between model predictions and evaluation scores，with a maximum increase of 15.42%. The validation results demonstrate the effectiveness of this method，providing a valuable reference for further research and applications in diesel engine noise quality evaluation.

关键词

柴油机 / 声品质 / 频率掩蔽 / 本征模态函数 / 主观特征描述

Key words

diesel engine / sound quality / frequency masking / intrinsic mode function(IMF) / subjective feature description

参考文献

[1] Steinbach L, Altinsoy M E. Prediction of annoyance evaluations of electric vehicle noise by using artificial neural networks[J]. Applied Acoustics, 2019, 145:149-158.

[2] Qian K, Hou Z. Intelligent evaluation of the interior sound quality of electric vehicles[J]. Applied Acoustics, 2021, 173:107684.

[3] Liu H, Zhang J, Guo P, et al. Sound quality prediction for engine—radiated noise[J]. Mechanical Systems and Signal Processing, 2015, 56:277-287.

[4] Tan Y, Yang J. Novel engine sound quality evaluation method in bench testing[J]. SAE Technical Paper, 2021(8):239685808.

[5] Wang Z H, Li P H, Liu H G, et al. Objective sound quality evaluation for the vehicle interior noise based on responses of the basilar membrane in the human ear[J]. Applied Acoustics, 2021, 172:107619.

[6] Yun D U, Lee S K. Objective evaluation of the knocking sound of a diesel engine considering the temporal and frequency masking effect simultaneously[J]. Journal of Sound and Vibration, 2017, 397:282-297.

[7] He Z, Guo H, Liu H, et al. A sound quality evaluation method for vehicle interior noise based on auditory loudness model[J]. Sound and Vibration, 2024, 58(1):47-58.

[8] Yang L, Wang P, Wang J. Research on evaluation model for vehicle interior sound quality based on an optimized BiLSTM using genetic algorithm[J]. Mechanical Systems and Signal Processing, 2023, 204:110827.

[9] Xie L, Lu C, Liu Z, et al. The evaluation of automobile interior acceleration sound fused with physiological signal using a hybrid deep neural network[J]. Mechanical Systems and Signal Processing, 2023, 184:109675.

[10] Huang X, Huang H, Wu J, et al. Sound quality prediction and improving of vehicle interior noise based on deep convolutional neural networks[J]. Expert Systems with Applications, 2020, 160:113657.

[11] Jin S P, Wang X F, Du L L, et al. Evaluation and modeling of automotive transmission whine noise quality based on MFCC and CNN[J]. Applied Acoustics, 2021, 172:107562.

[12] Qiu Y, Zhou E L, Xue H T, et al. Analysis on vehicle sound quality via deep belief network and optimization of exhaust system based on structure—SQE model[J]. Applied Acoustics, 2021, 171:107603.

[13] Liang K, Zhao H. Automatic evaluation of internal combustion engine noise based on an auditory model[J]. Shock and Vibration, 2019， 2019(1):2898219.

[14] Du J F, Yang P, Qu N F. Study on the sound quality of the electric vehicle powertrain under acceleration conditions[J]. Measurement, 2025, 239:115414.

[15] Huang H B, Li R X, Yang M L, et al. Evaluation of vehicle interior sound quality using a continuous restricted Boltzmann machine—based DBN[J]. Mechanical Systems and Signal Processing, 2017, 84:245-267.

[16] Lu Y, Zuo Y Y, Wang H, et al. Sound quality prediction for power coupling mechanism of HEV based on CEEMD—HT and RVM[J]. Neural Computing and Applications, 2021, 33(14):8201-8216.

[17] Huang H, Huang X, Su R, et al. Sound metric prediction of a power train system based on EEMD and GA—wavelet neural network[J]. Journal of Vibration and Shock, 2017, 36(9):130-137.

[18] 金可心, 卢海峰, 杨亮, 等. 融入时频能量特征的车内噪声声品质评价方法[J]. 电子测量与仪器学报, 2022, 36(5):96-103.

[19] Jin Kexin, Lu Haifeng, Yang Liang, et al. Sound quality evaluation method of vehicle interior noise based on time—frequency energy characteristics[J]. Journal of Electronic Measurement and Instrumentation, 2022, 36(5):96-103(in Chinese).

[20] Tang D, Bi F, Lin J, et al. Adaptive recursive variational mode decomposition for multiple engine faults detection[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71:1-11.

[21] Kim S Y, Ryu S C, Jun Y D, et al. Methodology for sound quality analysis of motors for automotive interior parts through subjective evaluation[J]. Sensors, 2022, 22(18):6898.

[22] Altinsoy M E, Jekosch U. The semantic space of vehicle sounds：Developing a semantic differential with regard to customer perception[J]. Journal of the Audio Engineering Society, 2012, 60(1/2):13-20.

[23] Ma C, Li Q, Deng L, et al. A novel sound quality evaluation method of the diagnosis of abnormal noise in interior permanent—magnet synchronous motors for electric vehicles[J]. IEEE Transactions on Industrial Electronics, 2017, 64(5):3883-3891.

[24] Fang Y, Zhang T. Sound quality investigation and improvement of an electric powertrain for electric vehicles[J]. IEEE Transactions on Industrial Electronics1， 2018, 65(2):1149-1157.

[25] Huang H, Wu J, Lim T C, et al. Pure electric vehicle nonstationary interior sound quality prediction based on deep CNNs with an adaptable learning rate tree[J]. Mechanical Systems and Signal Processing, 2021, 148:107170.

[26] Kwon G, Jo H, Kang Y J. Model of psychoacoustic sportiness for vehicle interior sound：Excluding loudness[J]. Applied Acoustics, 2018, 136:16-25.

[27] Chen P S, Xu L Y, Tang Q S, et al. Research on prediction model of tractor sound quality based on genetic algorithm[J]. Applied Acoustics, 2022, 185:108411.

[28] Du J, Yang P, Qu N. Study on the sound quality of the electric vehicle powertrain under acceleration conditions[J]. Measurement, 2024, 239:115414.

[29] Zhang X, Cheng J, Lu J, et al. Sound quality evaluation of pure electric vehicle with subjective and objective unified evaluation method[J]. International Journal of Vehicle Design, 2022, 88(2/3/4):283-303.

[30] Qian K, Hou Z, Sun Q, et al. Evaluation and optimization of sound quality in high—speed trains[J]. Applied Acoustics, 2021, 174:107830.

[31] Khan M S, Johansson Ö, Sundbäck U. Development of an annoyance index for heavy—duty diesel engine noise using multivariate analysis[J]. Noise Control Engineering Journal, 1997, 45(4):157-167.

[32] Hastings A L. Sound Quality of Diesel Engines[D]. West Lafayette： Purdue University, 2004.

[33] Wang Y, Xiang J, Markert R, et al. Spectral kurtosis for fault detection，diagnosis and prognostics of rotating machines：A review with applications[J]. Mechanical Systems and Signal Processing, 2016, 66:679-698.

[34] Sardar V M, Jadhav M L, Deshmukh S H. Use of median timbre features for speaker identification of whispering sound[C]// 3rd International Conference on Advanced Technologies for Societal Applications. Maharashtra，India， 2021:31-41.

[35] Zaw T H, War N. The combination of spectral entropy，zero crossing rate，short time energy and linear prediction error for voice activity detection[C]// 20th International Conference of Computer and Information Technology(ICCIT). Dhaka，People’s Republic of Bangladesh， 2017:23328916.

[36] Zhao Y, Zou M, Wang S. Wind power time series forecasting based on BP neural network[C]// 2023 4th International Symposium on Computer Engineering and Intelligent Communications(ISCEIC). Nanjing，China， 2023:439-443.

[37] Gao S Z, Li T C, Zhang Y M, et al. Fault diagnosis method of rolling bearings based on adaptive modified CEEMD and 1DCNN model[J]. ISA Transactions, 2023, 140:309-330.

基金资助

国家自然科学基金资助项目(51705357)