JTTs  Vol.5 No.2 , April 2015
Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model
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.

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.
References

[1]   Shu, G.Q., Wang, Y.J., Wei, H.Q. and Wang, Z.G. (2007) Measurement and Evaluation of Sound Quality of Interior Noise of Vehicle Cabin. Journal of Combustion Engine, 25, 77-83.

[2]   Chen, S.J. (2005) A Study on Sound Quality Preference Evaluation Car Interior Noise Based on Psycho-Acoustical Parameters. Shanghai Jiaotong University, Shanghai.

[3]   Shen, X.M., Zuo, S.G., Li, L. and Zhang, S.W. (2010) SVM Predictor of Vehicle Interior Sound Quality. Journal of Vibration and Shock, 29, 66-68.

[4]   Xu, Z.M., Xia, X.J., He, Y.S. and Zhang, Z.F. (2014) Analysis and Evaluation of Car Engine Starting Sound Quality. Journal of Vibration and Shock, 11, 142-147.

[5]   Wang, Y.S., Lee, C.-M., Kim, D.-G. and Xu, Y. (2007) Sound-Quality Prediction for Nonstationary Vehicle Interior Noise Based on Wavelet Pre-Processing Neural Network Model. Journal of Sound and Vibration, 299, 933-947.
http://dx.doi.org/10.1016/j.jsv.2006.07.034

[6]   Vapnik, V. (2000) The Nature of Statistical Learning Theory. Spinger Veflag, New York.

[7]   GB/T 18697-2002 (2002) Acoustics—Method for Measuring Vehicle Interior Noise.

[8]   ISO 5128-1980 (1980) Measurement of Noise inside Motor Vehicles.

[9]   ISO 362-1998 (1998) Measurement of Noise Emitted by Accelerating Road Vehicles—Engineering Method.

[10]   Robert, H.N. (1989) Theory of the Back-Propagation Neural Network. Neural Networks, 1, 593-605.

 
 
Top