Clear imaging beamforming method for the noise source identification in car cabin

Yuping  Wan; Jinfeng  Xia; Liang  Yang; Yang  Yang; Zhigang  Chu

doi:10.59400/sv1839

Clear imaging beamforming method for the noise source identification in car cabin

Yuping Wan State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing ChangAn Automobile Co., Ltd., Chongqing 401133, China
Jinfeng Xia State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing ChangAn Automobile Co., Ltd., Chongqing 401133, China
Liang Yang State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing ChangAn Automobile Co., Ltd., Chongqing 401133, China
Yang Yang Chongqing Automotive Power System Testing Engineering Technology Research Center, School of Vehicles Engineering, Chongqing Industry Polytechnic College, Chongqing 401120, China
Zhigang Chu College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China

Article ID: 1839

Keywords: noise control in car cabin; noise source identification; beamforming; spherical microphone array; clear imaging

Abstract

Noise in car cabin has a close relationship with ride comfort and noise control plays a key role in automobile product design. The prerequisite of noise control is to accurately identify noise sources. Due to the advantages of recording comprehensive sound field information and achieving panoramic source identification, beamforming with spherical microphone array is suitable for interior sound source identification. However, classic spherical harmonics beamforming (SHB) and filter and sum (FAS) suffer from wide mainlobes and serious sidelobe contamination. This paper proposes clear imaging beamforming method, which repeatedly removes the independent contribution of each source from SHB output based on coherence between sidelobe and mainlobe to improve imaging clarity. Simulations and experiments demonstrate that the proposed method has much clearer imaging than SHB and FAS, and could effectively identify the noise sources in car cabin.

References

[1]Huang L, Sun Z, Yu C, Zhang Y, and Yan B. The Noise exposure of urban rail transit drivers: hazard classification, assessment, and mitigation strategies. Appl. Sci-Basel. 2024, 14(16): 7388. DOI:10.3390/app14167388.

[2]Tao Y, Chai Y, Kou L, and Kwan M. Understanding noise exposure, noise annoyance, and psychologocal stress: incorporating individual mobility abd the temporality and the temporality of the exposure-effect relationship. Appl. Geogr. 2021, 125: 102283. DOI: 10.1016/j.apgeog.2020.102283.

[3]Wang A, Bista S, Can A, and Chaix B. Personal nise exposure during daily commutes and subjectively reported stress: A trip stage level analysis of mobilisense data. J. Transp. Health 2023, 30: 101612. DOI: 10.1016/j.jth.2023.101612.

[4]Battista G, Chiariotti P, and Castellini P. Spherical harmonics decomposition in inverse acoustic methods involving spherical arrays. J. Sound Vib. 2018, 433, 425-460. DOI: 10.1016/j.jsv.2018.05.001.

[5]Kreidl H. Increased spatial resolution for sound source location in the automotive industry with the help of new microphone arrays and optimized beamforming algorithms. In Proceedings of the 5th VDI Symposium on Maschinenakustik 2012: Competitive advantage due to low noise products, Karlsruhe, Germany, 13-14 November, 2012.

[6]Yin S, Chu Z, Zhang Y and Liu Y. Adaptive reweighting homotopy algorithm based compressive spherical beamforming with spherical microphone arrays. J. Acoust. Soc. Am. 2020, 147(1), 480-489. DOI: 10.1121/10.0000516

[7]Wang Y and Chen K. Sound field reconstruction within an entire cavity by plane wave expansion using a spherical microphone array. J. Acoust. Soc. Am. 2017, 142(2), 1858-1870. DOI: 10.1121/1.5006057

[8]Haddad, K, and Hald, J. 3D localization of acoustic sources with a spherical array. In Proceedings of the 7th European Conference on Noise Control 2008, Paris, France, 29 June-4 July, 2008.

[9]Park M and Rafaely B. Sound field analysis by plane-wave decomposition using spherical microphone array. J. Acoust. Soc. Am. 2005, 118(5), 3094-3103. DOI: 10.1121/1.2063108

[10]Meyer J and Elko G. A highly scalable spherical microphone array based on an orthonormal decomposition of the soundfield. In Proceedings of the 2002 IEEE International Conference on Acoustics, Speech and Signal Processing, Orlando, USA, 13-17 May, 2002.

[11]Chu Z, Yang Y and He Y. Deconvolution for three-dimensional acoustic source identification based on spherical harmonics beamforming. J. Sound Vib. 2015, 344, 484-502. DOI: 10.1016/j.jsv.2015.01.047.

[12]Chu Z, Zhao S, Yang Y and Yang YX. Deconvolution using CLEAN-SC for acoustic source identification with spherical microphone arrays. J. Sound Vib. 2019, 440, 161-173. DOI: 10.1016/j.jsv.2018.10.030.

[13]Chu Z, Yin S, Yang Y and Li P. Filter and sum based high resolution CLEAN-SC with spherical microphone arrays. Appl. Acoust. 2021, 182,108278. DOI: 10.1016/j.apacoust.2021.108278.

[14]Koretz A and Rafaely B. Dolph-Chebyshev beampattern design for spherical arrays. IEEE Trans. Signal Process. 2009, 57(6), 2417-2420. DOI: 10.1109/TSP.2009.2015120

[15]Yan S, Sun H, Svensson P, Ma X and Hoverm J. Optimal modal beamforming for spherical microphone arrays. IEEE-ACM Trans. Audio Speech Lang. 2011, 19(2), 361-371. DOI: 10.1109/TASL.2010.2047815

[16]Hald, J. Spherical beamforming with enhanced dynamic range. SAE Int. J. Passeng. Cars Mech. Syst. 2013, 6, 1334-1341. DOI: 10.4271/2013-01-1977

[17]Tiana-Roig. E, Torras-Rosell A, Fernandez-Grande. E, Jeong C, Agerkvist F. Enhancing the beamforming map of spherical arrays at low frequencies using acoustic holography. In Proceedings of the 5th Berlin Beamforming Conference 2014, Berlin, Germany, 19-20 February, 2014.

Published

2024-11-22

How to Cite

Wan, Y., Xia, J., Yang, L., Yang, Y., & Chu, Z. (2024). Clear imaging beamforming method for the noise source identification in car cabin . Sound & Vibration, 59(1), 1839. https://doi.org/10.59400/sv1839

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