scholarly journals MAP-Based Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and -Norm Minimization

2006 ◽  
Vol 2007 (1) ◽  
Author(s):  
Stefan Winter ◽  
Walter Kellermann ◽  
Hiroshi Sawada ◽  
Shoji Makino
Keyword(s):  
Blind Source Separation ◽  
Hierarchical Clustering ◽  
Source Separation ◽  
Underdetermined Blind Source Separation ◽  
Convolutive Mixtures ◽  
Norm Minimization

scholarly journals A Regularized Weighted Smoothed L0 Norm Minimization Method for Underdetermined Blind Source Separation

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4260 ◽  
Author(s):  
Linyu Wang ◽  
Xiangjun Yin ◽  
Huihui Yue ◽  
Jianhong Xiang
Keyword(s):  
Blind Source Separation ◽  
Signal Reconstruction ◽  
Source Separation ◽  
Minimization Method ◽  
Underdetermined Blind Source Separation ◽  
Weighted Function ◽  
Performance Simulation ◽  
Linear System Of Equations ◽  
Norm Minimization ◽  
Magnetic Resonance Imaging Mri

Compressed sensing (CS) theory has attracted widespread attention in recent years and has been widely used in signal and image processing, such as underdetermined blind source separation (UBSS), magnetic resonance imaging (MRI), etc. As the main link of CS, the goal of sparse signal reconstruction is how to recover accurately and effectively the original signal from an underdetermined linear system of equations (ULSE). For this problem, we propose a new algorithm called the weighted regularized smoothed L 0 -norm minimization algorithm (WReSL0). Under the framework of this algorithm, we have done three things: (1) proposed a new smoothed function called the compound inverse proportional function (CIPF); (2) proposed a new weighted function; and (3) a new regularization form is derived and constructed. In this algorithm, the weighted function and the new smoothed function are combined as the sparsity-promoting object, and a new regularization form is derived and constructed to enhance de-noising performance. Performance simulation experiments on both the real signal and real images show that the proposed WReSL0 algorithm outperforms other popular approaches, such as SL0, BPDN, NSL0, and L p -RLSand achieves better performances when it is used for UBSS.

scholarly journals Fault Feature Extraction for Reciprocating Compressors Based on Underdetermined Blind Source Separation

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1217
Author(s):  
Jindong Wang ◽  
Xin Chen ◽  
Haiyang Zhao ◽  
Yanyang Li ◽  
Zujian Liu
Keyword(s):  
Blind Source Separation ◽  
Hierarchical Clustering ◽  
Source Separation ◽  
Least Square Method ◽  
Least Square ◽  
Underdetermined Blind Source Separation ◽  
Matrix Estimation ◽  
Initial Cluster ◽  
Mixing Matrix ◽  
Source Signals

In practical engineering applications, the vibration signals collected by sensors often contain outliers, resulting in the separation accuracy of source signals from the observed signals being seriously affected. The mixing matrix estimation is crucial to the underdetermined blind source separation (UBSS), determining the accuracy level of the source signals recovery. Therefore, a two-stage clustering method is proposed by combining hierarchical clustering and K-means to improve the reliability of the estimated mixing matrix in this paper. The proposed method is used to solve the two major problems in the K-means algorithm: the random selection of initial cluster centers and the sensitivity of the algorithm to outliers. Firstly, the observed signals are clustered by hierarchical clustering to get the cluster centers. Secondly, the cosine distance is used to eliminate the outliers deviating from cluster centers. Then, the initial cluster centers are obtained by calculating the mean value of each remaining cluster. Finally, the mixing matrix is estimated with the improved K-means, and the sources are recovered using the least square method. Simulation and the reciprocating compressor fault experiments demonstrate the effectiveness of the proposed method.

scholarly journals An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gang Yu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Modal Parameters ◽  
Underdetermined Blind Source Separation ◽  
Structural Dynamic ◽  
Time Frequency ◽  
Modal Response ◽  
Experimental Validations ◽  
Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

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