Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model

Zutong Duan; Yansong Wang; Yanfeng Xing

doi:10.4236/jtts.2015.52013

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JTTs> Vol.5 No.2, April 2015

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Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model

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DOI: 10.4236/jtts.2015.52013 4,002 Downloads 4,530 Views Citations

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Zutong Duan, Yansong Wang^*, Yanfeng Xing

Affiliation(s)

College of Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China.

ABSTRACT

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.

KEYWORDS

Multiple Working Conditions, Neural Network, Back-Propagation, Sound Quality Prediction, Annoyance

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Duan, Z. , Wang, Y. and Xing, Y. (2015) Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model. Journal of Transportation Technologies, 5, 134-139. doi: 10.4236/jtts.2015.52013.

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