Page 82 - 《应用声学》2022年第3期

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evaluation of sound quality of abnormal noise from ve- sound quality[J]. Mechanical Systems and Signal Process-
hicle suspension shock absorber based on EMD-WVD[J]. ing, 2020, 138: 106549.
Journal of Vibration and Shock, 2015, 34(18): 154–160. [10] Qian K, Hou Z. Intelligent evaluation of the interior sound
[3] 刘海, 张广秀, 陈勇, 等. 运用核主成分分析提取内燃机声品 quality of electric vehicles[J]. Applied Acoustics, 2021,
质客观特征 [J]. 内燃机学报, 2019, 37(5): 441–445. 173: 107684.
Liu Hai, Zhang Guangxiu, Chen Yong, et al. Feature ex- [11] 胡溧, 杨航, 杨啟梁, 等. 怠速工况汽车空调声品质评价研
traction of engine sound quality by kernel principal com- 究 [J]. 汽车技术, 2021(2): 45–50.
ponent analysis[J]. Transactions of CSICE, 2019, 37(5): Hu Li, Yang Hang, Yang Qiliang, et al. Research on sound
441–445. quality evaluation of automobile air conditioner under idle
[4] Kwon G, Jo H, Kang Y J. Model of psychoacoustic sporti- condition[J]. Automotive Technology, 2021(2): 45–50.
ness for vehicle interior sound: excluding loudness[J]. Ap- [12] 董琦飞. 加速工况车内声品质的评价研究 [D]. 武汉: 湖北工
plied Acoustics, 2018, 136: 16–25. 业大学, 2014.
[5] 赖诗洋, 夏小均, 徐中明, 等. 汽车车窗升降声品质分析与评 [13] 王政钦, 毕锦烟, 黄涛, 等. 乘用车关门声品质风格划分评价
价 [J]. 汽车技术, 2018(7): 48–53. 研究 [J]. 汽车技术, 2020(7): 30–34.
Lai Shiyang, Xia Xiaojun, Xu Zhongming, et al. Analysis Wang Zhengqin, Bi Jinyan, Huang Tao, et al. Research on
and evaluation of power window sound quality in cars[J] stylized vehicle door closure sound quality evaluation[J].
Autombile Technology, 2018(7): 48–53. Automotive Technology, 2020(7): 30–34.
[6] 冯天培, 孙跃东, 王岩松, 等. 车内综合烦躁度评价的时序平 [14] Mosquera-Sánchez J A, Sarrazin M, Janssens K, et al.
滑激励级谱 CNN 模型 [J]. 汽车工程, 2020, 42(6): 784–792. Multiple target sound quality balance for hybrid electric
Feng Tianpei, Sun Yuedong, Wang Yansong, et al. An- powertrain noise[J]. Mechanical Systems and Signal Pro-
noyance evaluation model of vehicle interior noise based cessing, 2018, 99: 478–503.
on time-series smoothed excitation level spectrum CNN [15] Wang Y S, Guo H, Feng T P, et al. Acoustic behavior
mode[J]. Automotive Engineering, 2020, 42(6): 784–792. prediction for low-frequency sound quality based on finite
[7] 张勇, 王坤祥, 欧健, 等. 基于 RBF 神经网络的车内声品质预 element method and artificial neural network[J]. Applied
测及分析 [J]. 控制工程, 2019, 26(11): 2025–2030. Acoustics, 2017, 122: 62–71.
Zhang Yong, Wang Kunxiang, Ou Jian, et al. The ve- [16] Swart D J, Bekker A, Bienert J. The subjective dimen-
hicle interior sound quality prediction and analysis based sions of sound quality of standard production electric ve-
on RBF neural network[J]. Control Engineering of China, hicles[J]. Applied Acoustics, 2018, 129: 354–364.
2019, 26(11): 2025–2030. [17] 姜顺明, 王智锰. 采用听觉传感策略的声品质主动控制 [J]. 机
[8] 毕凤荣, 黄宇, 张立鹏, 等. 基于区间灰数理论的汽车声品质 械工程学报, 2019, 55(23): 147–153.
主观评价方法研究 [J]. 汽车工程, 2020, 42(7): 933–940. Jiang Shunming, Wang Zhimeng. Active sound quality
Bi Fengrong, Huang Yu, Zhang Lipeng, et al. Research control using auditory sensing strategy[J]. Journal of Me-
on subjective evaluation method of vehicle sound quality chanical Engineering, 2019, 55(23): 147–153.
based on interval grey number theory[J]. Automotive En- [18] Lee S, Lee G, Back J. Development of sound-quality in-
gineering, 2020, 42(7): 933–940. dexes in a car cabin owing to the acoustic characteristics
[9] Liao X, Zheng S. Quantification and characterization of of absorption materials[J]. Applied Acoustics, 2019, 143:
the role of subjective preferences on vehicle acceleration 125–140.