Page 155 - 《应用声学》2022年第4期
P. 155

第 41 卷 第 4 期              贾尚帅等: 利用卷积网络的高速列车主观声品质预测                                          653


             数与车内噪声的响度负相关系数最大,而与抖动度                                Shen Xiumin, Zuo Shuguang, Han Le, et al. Interior ve-
             的负相关系数最小。                                             hicle noise quality prediction using support vector ma-
                                                                   chines[J]. Journal of Vibration, Measurement & Diagno-
                 (4) 建立了基于 CNN 的声品质预测模型,将同
                                                                   sis, 2011, 31(1): 55–58, 128.
             时包含车内噪声时域和频域信息的时 -频分布图作                             [7] Fang Y, Zhang T. Sound quality investigation and im-
             为模型输入,模型更具有真实物理意义,预测精度                                provement of an electric powertrain for electric vehicles[J].
                                                                   IEEE Transactions on Industrial Electronics, 2018, 65(2):
             比 BP 神经网络模型更高,更适宜用于指导高速列
                                                                   1149–1157.
             车车内声品质的优化设计。                                        [8] Liu H, Zhang J H, Guo P, et al. Sound quality predic-
                                                                   tion for engine-radiated noise[J]. Mechanical Systems and
                                                                   Signal Processing, 2015, 56–57: 277–287.
                            参 考     文   献                        [9] Xing Y F, Wang Y S, Shi L, et al. Sound quality recogni-
                                                                   tion using optimal wavelet-packet transform and artificial
              [1] 代文强, 郑旭, 郝志勇, 等. 采用能量有限元分析的高速列                   neural network methods[J]. Mechanical Systems and Sig-
                 车车内噪声预测 [J]. 浙江大学学报 (工学版), 2019, 53(12):          nal Processing, 2016, 66–67: 875–892.
                 2396–2403.                                     [10] Pietila G, Lim T C. Intelligent systems approaches to
                 Dai Wenqiang, Zheng Xu, Hao Zhiyong, et al. Predic-  product sound quality evaluations-a review[J]. Applied
                 tion of high-speed train interior noise using energy finite  Acoustics, 2012, 73(10): 987–1002.
                 element analysis[J]. Journal of Zhejiang University (Engi-  [11] Zhang E L, Hou L, Shen C, et al. Sound quality prediction
                 neering Science), 2019, 53(12): 2396–2403.        of vehicle interior noise and mathematical modeling using
              [2] Luo L, Zheng X, Hao Z Y, et al. Sound quality evaluation  a back propagation neural network (BPNN) based on par-
                 of high-speed train interior noise by adaptive Moore loud-  ticles warm optimization (PSO)[J]. Measurement Science
                 ness algorithm[J]. Journal of Zhejiang University-Science  and Technology, 2016, 27(1): 015801.
                 A: Applied Physics & Engineering, 2017, 18(9): 690–703.  [12] Cao J W, Cao M, Wang J Z, et al. Urban noise recog-
              [3] 鞠龙华, 葛剑敏. 强噪声环境下高速列车内语言清晰度评价与                    nition with convolutional neural network[J]. Multimedia
                 分析 [J]. 同济大学学报 (自然科学版), 2017, 45(7): 994–999.     Tools and Applications, 2019, 78(20): 29021–29041.
                 Ju Longhua, Ge Jianmin.  Evaluation and analysis of  [13] Huang H B, Wu J H, Lim T C, et al. Pure electric vehi-
                 speech intelligibility in high-speed train compartments un-  cle nonstationary interior sound quality prediction based
                 der strong noise environment[J]. Journal of Tongji Univer-  on deep CNNs with an adaptable learning rate tree[J].
                 sity (Natural Science), 2017, 45(7): 994–999.     Mechanical Systems and Signal Processing, 2021, 148:
              [4] Park B, Jeon J, Choi S, et al. Short-term noise annoy-  107170.
                 ance assessment in passenger compartments of high-speed  [14] Otto N, Amman S, Eaton C, et al. Guidelines for jury
                 trains under sudden variation[J]. Applied Acoustics, 2015,  evaluations of automotive sounds[J]. SAE Technical Pa-
                 97: 46–53.                                        per 1999-01-1822, 1999.
              [5] Li J, Li L, Zhang Y X, et al. Annoyance evaluation of  [15] 孟凡雨. 高速列车车内声品质评价研究 [D] . 哈尔滨: 哈尔滨
                 noise emitted by urban substation[J]. Journal of Low Fre-  工业大学, 2013.
                 quency Noise, Vibration and Active Control, 2021, 40(4):  [16] Ferreira M D, Corrêa D C, Nonato L G, et al. Design-
                 2106–2114.                                        ing architectures of convolutional neural networks to solve
              [6] 申秀敏, 左曙光, 韩乐, 等. 基于支持向量机的车内噪声声品                  practical problems[J]. Expert Systems with Applications,
                 质预测 [J]. 振动、测试与诊断, 2011, 31(1): 55–58, 128.       2018, 94: 205–217.
   150   151   152   153   154   155   156   157   158   159   160