Penalty function beamforming sound source identification algorithm and its application

PDF(4184 KB)

Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (1) : 315-324.

ACOUSTIC RESEARCH AND APPLICATION

Penalty function beamforming sound source identification algorithm and its application

ZHAO Shen1,2, LI Wei1, QIN Yemei*1,2, ZHOU Kaijun1,2, LI Shiling1,2, SHI Shaojin1

Author information +

History +

Abstract

In the application of noise source identification based on microphone array, the function beamforming (FB) algorithm has the performance bottleneck of spatial resolution, so a penalty function beamforming(P-FB) algorithm was proposed. The normalized FB output is used as the penalty matrix, and the Hadamard product of the normalized beamforming matrix is calculated to adjust the steering vector in the weighted matrix. The adjusted steering vector and the cross-spectral exponential function are used to calculate the output of P-FB, so as to update the penalty matrix and implement iterative operation. By penalizing iterations, the point spread function can be made close to the Dirac function, and the spatial resolution performance can be improved. Simulations and experimental results show that the proposed algorithm exhibits a narrow main lobe, low side lobes, effectively addressing the issue of suboptimal spatial resolution in FB algorithm. In the application of sound source identification of compressed gas leakage, the algorithm can effectively suppress noise interference, reduce the main lobe, indicating that it has a good engineering application prospect.

Key words

sound source identification / function beamforming / penalty matrix / cross spectral matrix / normalization

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHAO Shen1, 2, LI Wei1, QIN Yemei1, 2, ZHOU Kaijun1, 2, LI Shiling1, 2, SHI Shaojin1. Penalty function beamforming sound source identification algorithm and its application[J]. Journal of Vibration and Shock, 2025, 44(1): 315-324

References

[1] CHIARIOTTI P, MARTARELLI M, CASTELLINI P. Acoustic beamforming for noise source localization: Reviews, methodology and applications[J]. Mechanical Systems and Signal Processing, 2019, 120: 422-448.
[2] 钱正莲, 杨亦春, 余立志, 等. 传声器阵列对高频弱声源的声成像识别方法[J]. 声学学报, 2015, 40(01): 90-96.
QIAN Zhenglian, YANG Yichun, YU Lizhi, et al. Identification and location of acoustic imaging on high frequency weak noise sources with a microphone array[J]. Acta Acustica, 2015, 40(01): 90-96.
[3] NING F, PAN F, ZHANG C, et al. A highly efficient compressed sensing algorithm for acoustic imaging in low signal-to-noise ratio environments[J]. Mechanical Systems and Signal Processing, 2018, 112: 113-128.
[4] DELVECCHIO S, BONFIGLIO P, POMPOLI F. Vibro-acoustic condition monitoring of Internal Combustion Engines: A critical review of existing techniques[J]. Mechanical Systems and Signal Processing, 2018, 99: 661-683.
[5] 褚志刚, 杨洋, 倪计民, 等. 波束形成声源识别技术研究进展[J]. 声学技术, 2013, 32(05): 430-435.
CHU Zhigang, YANG Yang, NI Jiming, et al. Review of beamforming based sound source identification techniques[J]. Technical Acoustics, 2013, 32(05): 430-435.
[6] MERINO-MARTINEZ R, SIJTSMA P, SNELLEN M, et al. A review of acoustic imaging methods using phased microphone arrays[J]. CEAS Aeronautical Journal, 2019, 10: 197-230.
[7] 杨洋, 褚志刚. 高性能波束形成声源识别方法研究综述[J]. 机械工程学报, 2021, 57(24): 166-183.
YANG Yang, CHU Zhigang. A review of high-performance beamforming methods for acoustic source identification[J]. Journal of Mechanical Engineering, 2021, 57(24): 166-183.
[8] 王月, 杨超, 王岩松, 等. 基于压缩聚焦网格点的快速反卷积算法[J]. 振动与冲击, 2022, 41(06): 250-255.
WANG Yue, YANG Chao, WANG Yangsong, et al. Fast deconvolution algorithm based on compressed focus grid points[J]. Journal of Vibration and Shock, 2022, 41(06): 250-255.
[9] 褚志刚, 余立超, 杨洋, 等. CLEAN-SC波束形成声源识别改进[J]. 振动与冲击, 2019, 38(15): 87-94.
CHU Zhigang, YU Lichao, YANG Yang, et al. Improved acoustic source identification based on CLEAN-SC beamforming[J]. Journal of Vibration and Shock, 2019, 38(15): 87-94.
[10] DOUGHERTY R P. Extensions of DAMAS and benefits and limitations of deconvolution in beamforming[C]// 11th AIAA/CEAS Aeroacoustics Conference. Monterey: AIAA-2005.
[11] EHRENFRIED E, KOOP L. Comparison of iterative deconvolution algorithms for the mapping of acoustic sources[J]. AIAA JOURNAL, 2007, 45(7): 1584–1595.
[12] 褚志刚, 杨洋. 基于非负最小二乘反卷积波束形成的发动机噪声源识别[J]. 振动与冲击, 2013, 32(23): 75-81.
CHU Zhigang, YANG Yang. Noise source identification for an engine based on FFT-non-negative least square (NNLS) deconvolution beamforming[J]. Journal of Vibration and Shock, 2013, 32(23): 75-81.
[13] LYLLOFF O, FERNANDEZ-GRANDE E. Improve the efficiency of deconvolution algorithms for soud source localization[J]. Journal Acoustical Society of America, 2015, 138(1): 172-180.
[14] SHEN L, CHU Z, YANG Y, et al. Periodic boundary based FFT-FISTA for sound source identification[J]. Applied Acoustics, 2018, 130: 87-91.
[15] MAYNARD J D, WILLIAMS E G, LEE Y. Nearfield acoustic holography: I. Theory of generalized holography and the development of NAH[J]. Journal of the Acoustical Society of America, 1985, 78(4): 1395-1413.
[16] BROOKS T F, HUMPHREYS W M. A deconvolution approach for the mapping of acoustic sources(DAMAS) determined from phased microphone arrays[J]. Journal of Sound and Vibration, 2006, 294(4): 856-879.
[17] XENAKI A, JACOBSEN F, TIANA-ROIG E, et al. Improving the resolution of beamforming measurements on wind turbines[C]// 20th International Congress on Acoustics. Sydney: ICA, 2010.
[18] 褚志刚, 段云炀, 沈林邦, 等. 函数波束形成声源识别性能分析及应用[J]. 机械工程学报, 2017, 53(04): 67-76.
CHU Zhigang, DUAN Yunyang, SHEN Linbang, et al. Performance analysis and application of functional beamforming soud source identification[J]. Journal of Mechanical Engineering, 2017, 53(04): 67-76.
[19] 陈思, 张志飞, 徐中明, 等. 基于高阶矩阵函数的广义逆波束形成改进算法[J]. 振动与冲击, 2017, 36(10): 98-103.
CHEN Si, ZHANG Zhifei, XU Zhongming, et al. Modified algorithm for generalized inverse beamforming based on high-order matrix function[J]. Journal of Vibration and Shock, 2017, 36(10): 98-103.
[20] RAMACHANDRAN R C, RAMAN G. On using functional beamforming to resolve noise sources on a large wind turbine[C]// 20th AIAA/CEAS Aeroacoustics Conference. Atlanta: AIAA, 2014.
[21] DOUGHERTY R P. Robust functional beamforming[C]// 9th Berlin Beamforming Conference. Berlin: BeBeC, 2022.
[22] YANG C, WANG Y, WANG Y, et al. An improved functional beamforming algorithm for far-field multi-sound source localization based on Hilbert curve[J]. Applied Acoustics, 2022, 192: 108729
[23] MERINO-MARTINEZ R, SNELLEN M, SIMONS D G. Functional beamforming applied to imaging of flyover noise on landing aircraft[J]. Journal of Aircraft, 2016, 53(6): 1830-1843.
[24] 赵慎, 李伟, 覃业梅, 等. 传声器阵列函数反卷积声源成像算法研究[J]. 仪器仪表学报, 2023, 44(10): 112-119.
ZHAO Shen, LI Wei, QIN Yemei, et al. Functional deconvolutional approach for the mapping of acoustic sources algorithm of microphone array[J]. Chinese Journal of Scientific Instrument, 2023, 44(10): 112-119.
[25] 王月, 杨超, 王岩松, 等. 基于互谱矩阵函数的多声源识别方法[J]. 振动、测试与诊断, 2023, 43(02): 277-281.
WANG Yue, YANG Chao, WANG Yansong, et al. Multi-source identification method based on cross-spectral matrix function[J]. Journal of Vibration, Measurement & Diagnosis, 2023, 43(2): 277-281.
[26] 黄琳森, 徐中明, 张志飞, 等. 快速迭代收缩阈值声源识别算法及其改进[J]. 仪器仪表学报, 2021, 42(02): 257-265.
HUANG Linsen, XU Zhongming, ZHANG Zhifei, et al. A fast iterative shrinkage threshold sound source identification algorithm and its improvement[J]. Chinese Journal of Scientific Instrument, 2021, 42(2): 257-265.
[27] SUZUKI T. L-1 generalized inverse beam-forming algorithm resolving coherent/incoherent, distributed and multipole sources[J]. Journal of Sound and Vibration, 2011, 330(24): 5835-5851.
[28] DOUGHERTY R P. Cross spectral matrix diagonal optimization[C]// 6th Berlin Beamforming Conference. Berlin: BeBeC, 2016.
[29] DOUGHERTY R P. Functional beamforming[C]// 5th Berlin Beamforming Conference. Berlin: BeBeC, 2014.
[30] DOUGHERTY R P. Functional beamforming for aeroacoustic source distributions[C]// 20th AIAA/CEAS Aeroacoustics Conference. Atlanta: AIAA, 2014.
[31] ZHANG Z, CHEN S, XU Z, et al. Iterative regularization method in generalized inverse beamforming[J]. Journal of Sound and Vibration, 2017, 396(26): 108-121.
[32] 徐中明, 李怡, 张志飞, 等. 弹性网正则化广义逆波束形成算法改进[J]. 仪器仪表学报, 2021, 42(06): 243-252.
XU Zhongming, LI Yi, ZHANG Zhifei, et al. Generalized inverse beamforming with improved elastic net regularization[J]. Chinese Journal of Scientific Instrument, 2021, 42(06): 243-252.
[33] MICHEL U. History of acoustic beamforming[C]// 1st Berlin Beamforming Conference. Berlin: BeBeC, 2006.
[34] MERINO-MARTINEZ R, SNELLEN M, SIMONS D G. Functional beamforming applied to full scale landing aircraft[C]// 6th Berlin Beamforming Conference. Berlin: BeBeC, 2016.
[35] CHU N, NING Y, YU L, et al. A high-resolution and low-frequency acoustic beamforming based on bayesian inference and non-synchronous measurements[J]. IEEE Access, 2020, 8: 82500-82513.