Resolution and quantification accuracy enhancement of functional delay and sum beamforming for three-dimensional acoustic source identification with solid spherical arrays

2017 ◽  
Vol 88 ◽  
pp. 274-289 ◽  
Author(s):  
Zhigang Chu ◽  
Yang Yang ◽  
Linbang Shen
Keyword(s):  
Source Identification ◽  
Three Dimensional ◽  
Acoustic Source ◽  
Accuracy Enhancement ◽  
Quantification Accuracy ◽  
Functional Delay ◽  
Spherical Arrays

scholarly journals Enhancement of two-dimensional acoustic source identification with Fourier-based deconvolution beamforming

2016 ◽  
Vol 18 (5) ◽  
pp. 3337-3361 ◽  
Author(s):  
Zhigang Chu ◽  
Yang Yang ◽  
Linbang Shen ◽  
Guoli Ping
Keyword(s):  
Source Identification ◽  
Two Dimensional ◽  
Acoustic Source

Sensor Planning for Model-Based Acoustic Source Identification

2020 ◽  
Author(s):  
Luke Calkins ◽  
Reza Khodayi-mehr ◽  
Wilkins Aquino ◽  
Michael M. Zavlanos
Keyword(s):  
Source Identification ◽  
Acoustic Source ◽  
Sensor Planning ◽  
Model Based

Three‐dimensional application of improved equivalent source method for noise source identification

2016 ◽  
Vol 52 (17) ◽  
pp. 1501-1503
Author(s):  
Shu Li ◽  
Zhongming Xu ◽  
Yansong He ◽  
Zhifei Zhang ◽  
Qinghua Wang
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Three Dimensional ◽  
Equivalent Source ◽  
Source Method ◽  
Noise Source Identification ◽  
Equivalent Source Method

Experiments on optimization of active control on a three‐dimensional acoustic source

1989 ◽  
Vol 85 (S1) ◽  
pp. S73-S73
Author(s):  
Jeffrey A. Giordano ◽  
Kenneth A. Cunefare ◽  
Gary Koopmann
Keyword(s):  
Active Control ◽  
Three Dimensional ◽  
Acoustic Source

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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