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风雨场中汽车乘员舱气动噪声声品质预测模型研究

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为研究雨天行驶时汽车的声品质,对风雨场中汽车乘员舱气动噪声信号进行声品质客观参量计算和主观评价,并对二者进行了相关分析。采用基于改进鲸鱼优化算法的反向传播(1WOA-BP)算法,以响度、粗糙度、抖动度、语音清晰度、语言干扰度和声压级6个客观参数为输入,以主观评分作为输出,建立预测模型,并与传统的反向传播(BP)神经网络预测模型和鲸鱼优化算法-反向传播(WOA-BP)预测模型进行了对比。结果表明,BP、WOA-BP、IWOA-BP算法的平均绝对百分比误差分别为28。33%、6。35%和2。82%,证明了基于IWOA-BP算法建立的风雨场中汽车乘员舱气动噪声声品质预测模型精度更高,效果更好。
Research on Prediction Model of Aerodynamic Noise Sound Quality of Automobile Cockpit in Wind-Rain Field
To study the sound quality of vehicle in rainy day driving,objective parameter calculation and subjective evaluation were conducted for the sound quality of the vehicle cockpit aerodynamic noise signals in the wind-rain field,with a correlation analysis between the two parameters.Based on the Improved Whale Optimization Algorithm-Back Propagation(IWOA-BP)algorithm,six objective parameters including loudness,roughness,jitter,speech intelligibility,speech interference and sound pressure level as input,and subjective scoring as output were used to establish a prediction model,which was compared with the traditional BP neural network prediction model and the WOA-BP prediction model.The results indicate that the mean absolute percentage error of BP,WOA-BP and IWOA-BP algorithms are 28.33%,6.35%and 2.82%respectively,proving that the sound quality prediction model of automobile cockpit aerodynamic noise in wind-rain field established based on IWOA-BP algorithm has a higher accuracy and a better effect.

Sound qualityWind-rain fieldAerodynamic noisePsychoacoustic parameterImproved Whale Optimization Algorithm(IWOA)algorithm

宗轶琦、张昊、许国猛、杨易、罗泽敏

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扬州大学,扬州 225127

湖南大学,长沙 410082

广州汽车集团股份有限公司汽车工程研究院,广州 511434

声品质 风雨场 气动噪声 心理声学参数 改进鲸鱼优化算法

国家自然科学基金项目国家自然科学基金项目

5187518651975197

2024

汽车技术
中国汽车工程学会 长春汽车研究所

汽车技术

CSTPCD北大核心
影响因子:0.522
ISSN:1000-3703
年,卷(期):2024.(5)
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