Abstract
Noise, vibration, and harshness (NVH) of electric automobiles is important because the loud NVH can reduce the satisfaction of automobile drivers and passengers. Therefore, the effective machine learning models to alleviate NVH is required. Although a huge amount of data is needed to construct the reliable models, the number of training data is very scarce in practice. In this paper, we propose a deep learning model combined with data augmentation methods (dropout and SpecAugment) that predicts interior noise levels from steering accelerometer signals when only a small number of training data is available. The effectiveness of the proposed framework was demonstrated using steering automobile accelerometer signals and noise levels from real automobiles.
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References
Chen, S.M., Wang, D.F., Zan, J.M.: Interior noise prediction of the automobile based on hybrid fe-sea method. Math. Prob. Eng. 2011 (2011)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256. JMLR Workshop and Conference Proceedings (2010)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hua, X., Thomas, A., Shultis, K.: Recent progress in battery electric vehicle noise, vibration, and harshness. Sci. Progress 104(1), 00368504211005224 (2021)
Huang, H.B., Wu, J.H., Huang, X.R., Yang, M.L., Ding, W.P.: The development of a deep neural network and its application to evaluating the interior sound quality of pure electric vehicles. Mech. Syst. Sig. Process. 120, 98–116 (2019)
Li, M., et al.: Vehicle interior noise prediction based on Elman neural network. Appl. Sci. 11(17), 8029 (2021)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Nor, M.J.M., Fouladi, M.H., Nahvi, H., Ariffin, A.K.: Index for vehicle acoustical comfort inside a passenger car. Appl. Acoust. 69(4), 343–353 (2008)
D.S. Park, et al.:. Specaugment: A simple data augmentation method for automatic speech recognition. arXiv preprint arXiv:1904.08779 (2019)
Park, D., Park, S., Kim, W., Rhiu, I., Yun, M.H.: A comparative study on subjective feeling of engine acceleration sound by automobile types. Int. J. Ind. Ergon. 74, 102843 (2019)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Ye, S., et al.: Transfer path analysis and its application in low-frequency vibration reduction of steering wheel of a passenger vehicle. Appl. Acoust. 157, 107021 (2020)
Zhenqi, Yu., Cheng, D., Huang, X.: Low-frequency road noise of electric vehicles based on measured road surface morphology. World Electric Veh. J. 10(2), 33 (2019)
Acknowledgments
This research was supported by the Brain Korea 21 FOUR, Ministry of Science and ICT (MSIT) in Korea under the ITRC support program (IITP-2020-0-01749) supervised by the Information & communications Technology Planning & Evaluation (IITP), and the National Research Foundation of Korea grant funded by the MSIT (NRF-2019R1A4A1024732).
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Jo, Y., Jeong, K., Ahn, S., Koh, E., Ko, E., Kim, S.B. (2023). Data Augmentation Methods for Electric Automobile Noise Design from Multi-Channel Steering Accelerometer Signals. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_49
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DOI: https://doi.org/10.1007/978-3-031-16072-1_49
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