摘要
为了高效而准确地评价与控制车内噪声品质,以B级车稳态工况下副驾位置的车内噪声为研究对象,采用等级评分法对采集到的声音样本进行了主观评价试验,同时计算了7个客观参数。以客观参量为输入,声品质主观结果为输出,引入基于遗传算法的BP神经网络建立了声品质预测模型。实验显示该模型输出结果与实际评分的相关系数达到0.928,检验组的预测最大误差为±8%。以所建模型的连接权值,分析了客观参数对主观评价结果的贡献度,并以影响系数较大的参数为输入重新构建了预测模型。研究结果表明:稳态工况下,车内声品质主要受响度、粗糙度和尖锐度的影响,其预测模型可由这3个参数来描述。
This paper carried out a subjective evaluation test with magnitude estimation for 78 noise samples to evaluate the sound quality of vehicles.In the test,six types of B-Class vehicles were taken as the study objects and sound signals collected in co-driver locations at steady states as experimental samples.Meanwhile,seven objective parameters were calculated to describe the sound characteristics.By using objective parameters as inputs,subjective values as outputs,a GA-BP neural network was adopted to establish a sound quality prediction model.Experiments show that the model gives good predictions of high correlation（0.928） and low error（±8%）.Then,the network connection coefficients were used to calculate the impact weight of objective parameters on the results of subjective evaluation,and a new model with main parameters was established.As expected,the loudness,sharpness and roughness with a total relative importance of 83% are the most influential parameters in vehicle interior sound quality.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2013年第2期462-468,共7页
Optics and Precision Engineering
基金
吉林省科技发展计划资助项目(No.20100361
No.20126007)
关键词
车内噪声
声品质预测
GA-BP神经网络
权重分析
vehicle interior noise
sound quality prediction
GA-BP neural network
weight analysis
