scholarly journals Correction to “A Fast Iterative Shrinkage/Thresholding Algorithm via Laplace Norm for Sound Source Identification”

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 130353-130353
Author(s):  
Linsen Huang ◽  
Zhongming Xu ◽  
Zhifei Zhang ◽  
Yansong He ◽  
Ming Zan
Keyword(s):  
Sound Source ◽  
Source Identification ◽  
Sound Source Identification ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

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.

scholarly journals A Ratio Model of L1/L2 Norm for Sound Source Identification

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5290
Author(s):  
Linsen Huang ◽  
Zhongming Xu ◽  
Zhifei Zhang ◽  
Yansong He
Keyword(s):  
Sound Source ◽  
Source Identification ◽  
Regularization Parameter ◽  
Monte Carlo Analysis ◽  
Tikhonov Regularization Method ◽  
Ratio Model ◽  
Accurate Identification ◽  
Sound Source Identification ◽  
L2 Norm ◽  
Thresholding Algorithm

In the field of sound source identification, robust and accurate identification of the targeted source could be a challenging task. Most of the existing methods select the regularization parameters whose value could directly affect the accuracy of sound source identification during the solving processing. In this paper, we introduced the ratio model ℓ1/ℓ2 norm to identify the sound source(s) in the engineering field. Using the alternating direction method of multipliers solver, the proposed approach could avoid the selection of the regularization parameter and localize sound source(s) with robustness at low and medium frequencies. Compared with other three methods employing classical penalty functions, including the Tikhonov regularization method, the iterative zoom-out-thresholding algorithm and the fast iterative shrinkage-thresholding algorithm, the Monte Carlo Analysis shows that the proposed approach with ℓ1/ℓ2 model leads to stable sound pressure reconstruction results at low and medium frequencies. The proposed method demonstrates beneficial distance-adaptability and signal-to-noise ratio (SNR)-adaptability for sound source identification inverse problems.

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

Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. V99-V113 ◽  
Author(s):  
Zhong-Xiao Li ◽  
Zhen-Chun Li
Keyword(s):  
Field Data ◽  
Optimization Problem ◽  
Computational Cost ◽  
Computation Time ◽  
Blind Separation ◽  
L1 Norm ◽  
Norm Minimization ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational cost because the IRLS algorithm achieves nonquadratic optimization with an LS optimization problem solved in each iteration. In general, it is good to have a faster 3D BSCM method. To improve the adaptability of field data processing, the fast iterative shrinkage thresholding algorithm (FISTA) is introduced into the 3D BSCM method. The proximity operator of FISTA can solve the L1-norm minimization problem efficiently. We demonstrate that our FISTA-based 3D BSCM method achieves similar accuracy of estimating primaries as that of the reference IRLS-based 3D BSCM method. Furthermore, our FISTA-based 3D BSCM method reduces computation time by approximately 60% compared with the reference IRLS-based 3D BSCM method in the synthetic and field data examples.

scholarly journals Fast Algorithms for the Anisotropic LLT Model in Image Denoising

2011 ◽  
Vol 1 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Zhi-Feng Pang ◽  
Li-Lian Wang ◽  
Yu-Fei Yang
Keyword(s):  
Convergence Rate ◽  
Image Denoising ◽  
Projection Method ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Minimization Problems ◽  
Iterative Shrinkage ◽  
Speed Up ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

AbstractIn this paper, we propose a new projection method for solving a general minimization problems with twoL1-regularization terms for image denoising. It is related to the split Bregman method, but it avoids solving PDEs in the iteration. We employ the fast iterative shrinkage-thresholding algorithm (FISTA) to speed up the proposed method to a convergence rateO(k−2). We also show the convergence of the algorithms. Finally, we apply the methods to the anisotropic Lysaker, Lundervold and Tai (LLT) model and demonstrate their efficiency.

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