高速列车车内声品质评价综述

钱堃; 沈政华; 谭璟; 刘珂; 段继英; 杜习康; 赵剑

doi:10.19818/j.cnki.1671-1637.2024.05.011

高速列车车内声品质评价综述

doi: 10.19818/j.cnki.1671-1637.2024.05.011

大连理工大学机械工程学院，辽宁大连 116023

基金项目:

国家重点研发计划 2019YFE0121300

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 2019M650657

详细信息

作者简介:
钱堃(1988-)，男，吉林桦甸人，大连理工大学副研究员，工学博士，从事车辆振动噪声分析与控制研究

通讯作者:
赵剑(1980-)，男，河北石家庄人，大连理工大学教授，工学博士

中图分类号: U270.16
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- 文章访问数: 9
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- 被引次数: 0
出版历程
- 收稿日期: 2024-04-21
- 网络出版日期: 2024-12-20
- 刊出日期: 2024-10-25

Review on evaluation of sound quality in high-speed trains

School of Mechanical Engineering, Dalian University of Technology, Dalian 116023, Liaoning, China

Funds:

National Key Research and Development Program of China 2019YFE0121300

Fundamental Research Funds for the Central Universities DUT22RC(3)002

China Postdoctoral Science Foundation 2019M650657

More Information

Author Bio:
QIAN Kun(1988-), male, associate professor, PhD, qiankun_nvh@163.com

ZHAO Jian(1980-), male, professor, PhD, jzhao@dlut.edu.cn

摘要

摘要: 从声品质客观评价、声品质主观评价、声品质客观量化模型三方面介绍了当前高速列车车内声品质评价研究现状和结果，归纳了高速列车车内声品质主、客观评价方法，总结了高速列车声品质客观量化模型，探讨了不同模型的优缺点，展望了高速列车车内声品质评价未来的发展方向。分析结果表明：现阶段高速列车声学标准和声学设计目标大多采用A计权声压级作为车内噪声评价指标，但在大多数情况下高速列车车内噪声以中低频率为主，此时A计权声压级不能很好地表征人耳对高速列车车内噪声的主观感受，应考虑使用声品质对高速列车车内噪声进行主、客观评价；未来应重点关注声品质客观参量对高速列车车内声品质适用性的研究，如何提取关键的声品质客观参量是高速列车车内声品质评价研究的重要方向之一；现有的传统客观心理声学参量不能很好地与机器学习模型结合实现声品质的准确评价，将传统声音信号进行特征提取，并结合机器学习模型进行声品质评价分析是未来高速列车车内声品质评价的发展趋势；传统的高速列车车内声品质主观评价方法评价时间长，可重复性差；建立高精度声品质客观量化模型代替传统主观评价方法，以缩短评价时间，提高评价准确性，是未来高速列车车内声品质评价研究的重点方向；传统的多元线性回归模型不能很好地评价高速列车车内声品质，随着机器学习的迅速发展，未来选择合适的机器学习模型结合智能算法优化，开发更准确、高效的声品质评价预测模型是高速列车车内声品质研究的重要内容。
- 高速列车 /
- 噪声 /
- 声品质 /
- 评价模型 /
- 神经网络 /
- 智能算法
Abstract: The current research status and results of the evaluation of sound quality in high-speed trains were introduced from three aspects, including objective evaluation, subjective evaluation, and objective quantitative model. The subjective and objective evaluation methods of sound quality, as well as the objective quantitative models of sound quality in high-speed trains were summarized. The advantages and disadvantages of different models were discussed, and the future development directions of evaluation of sound quality in high-speed trains were forecasted. Analysis results show that at the present stage, most of the acoustic standards and acoustic design objectives for high-speed trains use A-weighted sound pressure level as the interior noise evaluation index, but in most cases, the noise is dominated by the low and medium frequencies. At this time, the A-weighted sound pressure level fails to well characterize the human ear's subjective feeling of the noise, so the use of the sound quality should be considered to carry out the subjective and objective evaluations of the noise. In the future, it is necessary to pay more attention to the applicability study of the objective parameters of the sound quality on the sound quality in high-speed trains, and the extraction of the key objective parameters of the sound quality is one of the important directions of the research on the evaluation of sound quality. The existing traditional objective psychoacoustic parameters cannot be well combined with the machine learning model to realize the accurate evaluation of sound quality. Extracting the features of traditional sound signals and combining it with the machine learning model for sound quality evaluation and analysis are the development trends of the future evaluation of sound quality in high-speed trains. The traditional subjective evaluation method of sound quality in high-speed trains has a long evaluation time and poor repeatability. Therefore, establishing a high-precision objective quantitative model of sound quality to replace traditional subjective evaluation methods, shorten the evaluation time, and improving the evaluation accuracy is the key direction of the future research on the evaluation of sound quality. The traditional multivariate linear regression model fails to evaluate the sound quality in the high-speed train well. In the future, with the rapid development of machine learning, the selection of suitable machine learning models combined with intelligent algorithm optimization to develop more accurate and efficient sound quality evaluation prediction models is an important part of the research on sound quality in high-speed trains. 8 tabs, 22 figs, 67 refs.
- high-speed train /
- noise /
- sound quality /
- evaluation model /
- neural network /
- intelligent algorithm

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图 1 高速列车车外噪声与速度关系

Figure 1. Relationship between outside noise and speed of high-speed train

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图 2 噪声A计权声压级与主观烦恼度相关性

Figure 2. Correlation between noise A-W SPL and subjective annoyance degree

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图 3 噪声A计权声压级、说话噪声与主观烦恼度相关性

Figure 3. Correlation of noise A-W SPL, speaking noise and subjective annoyance degree

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图 4 乘客室噪声频谱随速度的变化曲线

Figure 4. Variation curves of noise spectrum with speed in passenger compartment

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图 5 客室某测点噪声特性

Figure 5. Noise characteristics of a measuring point in passenger compartment

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图 6 观光区1/3倍频程频谱

Figure 6. 1/3 octave spectrum of sightseeing area

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图 7 匀速330 km·h^-1工况下高速列车司机室内噪声特性

Figure 7. Noise characteristics of high-speed train driver's cabs at a constant speed of 330 km·h^-1

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图 8 不同速度下高速列车司机室内特征响度曲线

Figure 8. Characteristic loudness curves of high-speed train driver's cabs at different speeds

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图 9 匀速350 km·h^-1工况下复兴号动车组各测点噪声1/3倍频程

Figure 9. 1/3 octave of noise at each measurement point of Fuxing electric multiple units at a constant speed of 350 km·h^-1

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图 10 高速动车组车内尖锐度和运行速度关系曲线

Figure 10. Relation curves between inside sharpness and running speed of high-speed trains

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图 11 客观参量与主观评价相关性

Figure 11. Correlation between objective parameter and subjective evaluation

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图 12 高速列车观光区和飞机舱内噪声显著频带对比

Figure 12. Comparison of significant frequency bands of noise in high-speed train sightseeing area and aircraft cabin

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图 13 车内噪声倍频程声压级评估

Figure 13. Estimation of interior noise octave sound pressure level

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图 14 主观评价流程

Figure 14. Flow of subjective evaluation

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图 15 动车组各位置噪声主观评价值与运行速度

Figure 15. Subjective evaluation values and running speeds of each position noise of high-speed trains

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图 16 语意细分法5等级标准

Figure 16. Five level standard of semantic subdivision method

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图 17 响度与主观评价值相关性

Figure 17. Correlation between loudness and subjective valuation

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图 18 分组成对比较法

Figure 18. Group pair comparison method

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图 19 多元线性回归模型预测结果

Figure 19. Prediction results of multiple linear regression model

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图 20 支持向量机回归预测

Figure 20. Regression prediction of SVM

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图 21 随机森林预测模型

Figure 21. Prediction model of random forest

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图 22 双耳梅尔尺度频率倒谱系数-卷积神经网络音质预测模型

Figure 22. Sound quality prediction model of binaural MFCC-CNN

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表 1 车内噪声限值要求

Table 1. Limits of noise in high-speed trains dB(A)

列车速度/(km·h^-1)			200~250		300		350
噪声评价			优	良	优	良	优	良
车内区域	司机室		75	77	77	79	79	80
	乘客室	端部	67	69	70	72	72	74
	乘客室	中部	65	67	68	70	70	72

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表 2 UIC 660—2002噪声限值(车内)

Table 2. UIC 660—2002 noise limits (in high-speed trains)

运行条件	明线上	隧道内	静止
限值/ dB(A)	78	83	68

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表 3 UIC 651—2002噪声限值(司机室)

Table 3. UIC 651—2002 noise limits (in driver's cabs)

运行条件/(km·h^-1)	≤250	≤300	静止
限值/ dB(A)	65	68	55

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表 4 7种参量的定义与优缺点

Table 4. Definitions, advantages and disadvantages of seven parameters

心理声学参量	定义	优点	缺点
A声级	使用A计权网络对噪声进行加权处理，强化人耳可听范围	可以在一定程度上表示人耳对噪声的烦恼程度	对低频成分高的噪声评价结果不准确
响度	表示声音强弱的属性	对声音音量大小的准确评价，可评估声音的响亮程度和强度	计算方法和标准存在一定的主观性和不确定性，需要复杂的算法和计算资源
尖锐度	描述声音信号中高频部分含量的客观参量	对声音高频成分的准确评价，用于评估声音的清晰度和细节表现	计算方法和标准存在一定的主观性和不确定性，需要复杂的算法和计算资源
粗糙度	描述调制后的声信号给人的听觉感受的客观参量	准确地反映声音的质量和稳定性	只能提供声音定量评价，无法提供声音的主观感受
抖动度	描述调制后的声信号给人的听觉感受的客观参量	准确地反映声音的质量和稳定性	只能提供声音定量评价，无法提供声音的主观感受
音调度	描述声音高低的物理量	对声音音高进行准确评价	但计算方法和标准存在一定主观性和不确定性
语言清晰度指数	有噪声的环境下对说话的清晰度进行评价描述的心理声学参数	可以对语音的可懂程度的评价	评价存在一定的主观性和不确定性

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表 5 脑电波与对应的生理反馈

Table 5. Brain waves and corresponding physiological feedbacks

脑电波主要成分	频率/Hz	振幅/μV	精神状态
δ	[1,4]	[10,20]	昏睡
θ	(4,8]	[20,40]	睡眠、疲劳
α	(8,12]	[25,100]	清醒、静息
β	(12,30]	[5,30]	兴奋、紧张
γ	(30, 50]		警觉、记忆

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表 6 主观评价方法优缺点及应用现状

Table 6. Advantages and disadvantages of subjective evaluation methods and current status of application

评价方法	优点	缺点	应用现状
等级评分法	简单快捷，工作量小	声音样本不能重复播放，对被试者有一定要求	在高速列车车内声品质评价中应用广泛
语义细分法	具有很好的稳定性	对主观评价人员要求较高，需要评价人员对声音的不同特征选择给定的对应形容词，一般只适用于受过训练的专业人员	在高速列车车内声品质研究中应用较少
成对比较法	对被试者要求较低，两两组合评判可以保证整体评判准确性	样本数量较多时评价结果不准确	在国内外高速列车车内声品质评价及异响声音评价中被广泛应用

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表 7 不同模型优势、局限性及适用前景

Table 7. Strengths, limitations and application prospects of different models

模型	优势	局限性	适用前景
多元线性回归预测模型	计算简单，复杂性低	不能准确拟合复杂非线性关系	客观参量与主观评价的相关性分析
支持向量机预测模型	适用性广泛，鲁棒性强，非线性分类能力强，泛化能力强	计算复杂度高，参数选择困难，只适用于二分类问题	简单分类问题以及相对复杂的关系预测
BP神经网络预测模型	非线性建模能力强，适用性广泛，学习能力强	训练时间长，参数选择困难，易陷入局部最优解	复杂的客观参量与主观评价之间的关系预测
随机森林预测模型	处理高维度数据、缺失数据、不平衡数据集，可以评估特征重要性	解释性差，处理大规模数据时占用内存大，对噪声数据敏感	噪声特征重要性评估
卷积神经网络预测模型	自动学习数据特征，在图像处理、语音识别领域表现优异，泛化性较强，速度较快，具有一定的鲁棒性	需要大量的数据和计算资源进行训练，难以解释，对输入数据尺寸和形状敏感，需要对数据进行预处理	声音信号图片形式的处理与分析

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表 8 噪声舒适性分级

Table 8. Classification of noise comfort

噪声舒适性均值	评价程度	级别
[0, 1]	非常舒适	1
(1, 2]	舒适	2
(2, 3]	较舒适	3
(3, 4]	不舒适	4
(4, 5]	非常不舒适	5

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