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Adaptive Active Vehicle Interior Noise Control Algorithm Based on Nonlinear Signal Reconstruction

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Abstract

In this study, to reduce secondary sound source pollution in the reference signal of active noise control (ANC), a novel ANC algorithm, based on signal reconstruction, is proposed for vehicle interior noise. This algorithm combines the processes of ear-sides noise reconstruction and ANC. First, to reduce non-stationarity and nonlinearity, multi-source noise signals outside the vehicle are decomposed into a finite number of intrinsic mode function (IMF) components by empirical mode decomposition (EMD). Second, the IMFs are reconstructed by the energy-extreme division method into three components: high-frequency, intermediate-frequency and low-frequency. The radial basis function neural network (RBFNN) parameters are adjusted by the proportions of the components. Model training is performed to obtain the high-precision EMD–RBFNN reconstruction model (EMD–NNRM). The reconstructed noise signal is used as the reference signal of the variable step-size least mean square (VSS-LMS) algorithm, to control the passenger ear-sides noise. The effectiveness of the EMD–NNRM is validated using four noise signals from the outside of a vehicle. The interior noise of a high-speed vehicle is processed by the proposed algorithm and the traditional VSS-LMS algorithm for comparison. The reconstruction results show that the mean absolute error is improved by 77.64% compared with the back propagation neural network reconstruction model. Reconstructed passenger ear-sides noise can be utilized for ANC. The active control results suggest that the proposed algorithm can not only effectively suppress the interior noise but can also avoid pollution from secondary sound sources.

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Data and Code Availability

The authors declare that data and code are true and available.

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Acknowledgements

This work was supported by the Project of National Natural Science Foundation of China (No. 51675324) and partly supported by the Project of Shanghai Automotive Industry Sci-Tech Development Foundation (No. 1523) and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, China.

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, No.333, Longteng Road, Songjiang District, Shanghai, 201620, People’s Republic of China

    Xiaolan Wang, Tongzhou Wang, Yansong Wang, Dongpo Yang, Chao Yang & Ningning Liu

  2. CATARC (Tianjin) Automotive Engineering Research Institute, No.68, Xianfeng East Road, Dongli District, Tianjin, 300300, People’s Republic of China

    Lili Su

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  1. Xiaolan Wang
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Contributions

XW Conceptualization, Methodology. TW Data curation, Writing- Original draft preparation. LS Writing- Reviewing and Editing. YW Supervision. DY Testing, Software, Validation. CY Visualization, Investigation. NL Testing, Software, Validation.

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Correspondence to Xiaolan Wang.

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Wang, X., Wang, T., Su, L. et al. Adaptive Active Vehicle Interior Noise Control Algorithm Based on Nonlinear Signal Reconstruction. Circuits Syst Signal Process 39, 5226–5246 (2020). https://doi.org/10.1007/s00034-020-01410-0

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