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A Study on Active Noise Reduction of Automobile Engine Compartment Based on Adaptive LMS Algorithm

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Abstract

The engine compartment is the main source of automobile noise. In this paper, based on the study of the noise characteristics of the engine compartment, the relationship model is established between engine speed and noise frequency, and an active noise reduction method is proposed based on the adaptive least mean square (LMS) algorithm. Because the adaptive change of the convergence factor is controlled by noise error, the method can reduce the steady-state error of the algorithm and improve the convergence speed of the algorithm. Through the analysis of single-channel active noise reduction control method, we deduced that this method can also realize multi-channel and multi-frequency point active noise reduction. Simulation and experimental results show that the convergence rate and steady-state error of the adaptive LMS algorithm are both taken into account in our method; the two-channel noise reduction experiment also shows that the total sound pressure level of channel one and channel two is reduced by 4.6 and 9.6 dB, respectively, which fully shows the feasibility of the multi-channel active noise reduction method based on the adaptive LMS algorithm.

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Funding

This study is supported by Fund project: Key project of the 2016 excellent young talents support program for colleges and universities in Anhui province (Grant No: gxyqZD2016247); Key project of 2019 excellent middle-aged and young backbone talents of colleges and universities visiting and studying at home and abroad (Grant No: gxgnfx2019028); Teaching and research project electrical and electronic technology (Grant No: 2016mooc312).

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  1. School of Electrical and Optical Engineering, West Anhui University, Lu’an, 237012, Anhui, China

    Congbing Wu & Ping Yu

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  1. Congbing Wu
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  2. Ping Yu
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Correspondence to Congbing Wu.

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Wu, C., Yu, P. A Study on Active Noise Reduction of Automobile Engine Compartment Based on Adaptive LMS Algorithm. Acoust Aust 48, 431–440 (2020). https://doi.org/10.1007/s40857-020-00198-y

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  • DOI: https://doi.org/10.1007/s40857-020-00198-y

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