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

Journal of Transportation Technologies > Vol.5 No.2, April 2015

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

Zutong Duan, Yansong Wang^*, Yanfeng Xing

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

DOI: 10.4236/jtts.2015.52013 PDF HTML XML 4,167 Downloads 4,746 Views Citations

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

Share and Cite:

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.

Conflicts of Interest

The authors declare no conflicts of interest.

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