Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model
Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model作者机构：College of Automotive Engineering Shanghai University of Engineering Science Shanghai China
出 版 物：《Journal of Transportation Technologies》 (交通科技期刊（英文）)
年 卷 期：2015年第5卷第2期
页 面：134-139页
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
主 题：Multiple Working Conditions Neural Network Back-Propagation Sound Quality Prediction Annoyance
摘 要：This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.
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