Doppler effect in the time-domain beamforming for rotating sound source identification

2020 ◽  
Vol 148 (1) ◽  
pp. 430-443 ◽  
Author(s):  
Wei Ma ◽  
Ce Zhang
Keyword(s):  
Sound Source ◽  
Time Domain ◽  
Source Identification ◽  
Doppler Effect ◽  
Sound Source Identification ◽  
The Time Domain

scholarly journals An investigation into high-speed train interior noise with operational transfer path analysis method

2021 ◽  
Author(s):  
Muxiao Li ◽  
Ziwei Zhu ◽  
Tiesong Deng ◽  
Xiaozhen Sheng
Keyword(s):  
Sound Source ◽  
Reference Point ◽  
Source Identification ◽  
High Speed ◽  
Interior Noise ◽  
Target Point ◽  
High Speed Train ◽  
Transfer Path ◽  
Sound Source Identification ◽  
Transfer Path Analysis

AbstractPassengers' demands for riding comfort have been getting higher and higher as the high-speed railway develops. Scientific methods to analyze the interior noise of the high-speed train are needed and the operational transfer path analysis (OTPA) method provides a theoretical basis and guidance for the noise control of the train and overcomes the shortcomings of the traditional method, which has high test efficiency and can be carried out during the working state of the targeted machine. The OTPA model is established from the aspects of "path reference point-target point" and "sound source reference point-target point". As for the mechanism of the noise transmission path, an assumption is made that the direct sound propagation is ignored, and the symmetric sound source and the symmetric path are merged. Using the operational test data and the OTPA method, combined with the results of spherical array sound source identification, the path contribution and sound source contribution of the interior noise are analyzed, respectively, from aspects of the total value and spectrum. The results show that the OTPA conforms to the calculation results of the spherical array sound source identification. At low speed, the contribution of the floor path and the contribution of the bogie sources are dominant. When the speed is greater than 300 km/h, the contribution of the roof path is dominant. Moreover, for the carriage with a pantograph, the lifted pantograph is an obvious source. The noise from the exterior sources of the train transfer into the interior mainly through the form of structural excitation, and the contribution of air excitation is non-significant. Certain analyses of train parts provide guides for the interior noise control.

Sound Source Identification Using Coherence- and Intensity-Based Methods

2007 ◽  
Vol 56 (6) ◽  
pp. 2478-2485 ◽  
Author(s):  
Giovanni Moschioni ◽  
Bortolino Saggin ◽  
Marco Tarabini
Keyword(s):  
Sound Source ◽  
Source Identification ◽  
Sound Source Identification

scholarly journals Improving the Sound Source Identification Performance of Sparsity Constrained Deconvolution Beamforming Utilizing SFISTA

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Linbang Shen ◽  
Zhigang Chu ◽  
Long Tan ◽  
Debing Chen ◽  
Fangbiao Ye
Keyword(s):  
Sound Source ◽  
Source Identification ◽  
Theoretical Background ◽  
Identification Performance ◽  
Sound Source Identification ◽  
Iterative Shrinkage ◽  
Quantification Accuracy ◽  
Smoothing Parameters ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

In this paper, an alternative sparsity constrained deconvolution beamforming utilizing the smoothing fast iterative shrinkage-thresholding algorithm (SFISTA) is proposed for sound source identification. Theoretical background and solving procedures are introduced. The influence of SFISTA regularization and smoothing parameters on the sound source identification performance is analyzed, and the recommended values of the parameters are obtained for the presented cases. Compared with the sparsity constrained deconvolution approach for the mapping of acoustic sources (SC-DAMAS) and the fast iterative shrinkage-thresholding algorithm (FISTA), the proposed SFISTA with appropriate regularization and smoothing parameters has faster convergence speed, higher quantification accuracy and computational efficiency, and more insensitivity to measurement noise.

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