The Research and Simulation of Blind Source Separation Algorithm

2016 ◽  
Vol 8 (3) ◽  
pp. 1-36
Author(s):  
Tao Gao ◽  
Jincan Li
Keyword(s):  
Blind Source Separation ◽  
Speech Enhancement ◽  
Source Separation ◽  
Separation Algorithm ◽  
Original Source ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Performance Evaluation Index ◽  
The Mathematical Model ◽  
Source Signals

When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

The Research and Simulation of Blind Source Separation Algorithm

2020 ◽  
pp. 494-531
Author(s):  
Tao Gao ◽  
Jincan Li
Keyword(s):  
Blind Source Separation ◽  
Speech Enhancement ◽  
Source Separation ◽  
Separation Algorithm ◽  
Original Source ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Performance Evaluation Index ◽  
The Mathematical Model ◽  
Source Signals

When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

scholarly journals Low-Element Image Restoration Based on an Out-of-Order Elimination Algorithm

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1192 ◽  
Author(s):  
Yaqin Xie ◽  
Jiayin Yu ◽  
Xinwu Chen ◽  
Qun Ding ◽  
Erfu Wang
Keyword(s):  
Image Restoration ◽  
Blind Source Separation ◽  
Source Separation ◽  
Signal Recovery ◽  
Separation Performance ◽  
Underdetermined Blind Source Separation ◽  
Elimination Algorithm ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Source Signals

To reduce the consumption of receiving devices, a number of devices at the receiving end undergo low-element treatment (the number of devices at the receiving end is less than that at the transmitting ends). The underdetermined blind-source separation system is a classic low-element model at the receiving end. Blind signal extraction in an underdetermined system remains an ill-posed problem, as it is difficult to extract all the source signals. To realize fewer devices at the receiving end without information loss, this paper proposes an image restoration method for underdetermined blind-source separation based on an out-of-order elimination algorithm. Firstly, a chaotic system is used to perform hidden transmission of source signals, where the source signals can hardly be observed and confidentiality is guaranteed. Secondly, empirical mode decomposition is used to decompose and complement the missing observed signals, and the fast independent component analysis (FastICA) algorithm is used to obtain part of the source signals. Finally, all the source signals are successfully separated using the out-of-order elimination algorithm and the FastICA algorithm. The results show that the performance of the underdetermined blind separation algorithm is related to the configuration of the transceiver antenna. When the signal is 3 × 4 antenna configuration, the algorithm in this paper is superior to the comparison algorithm in signal recovery, and its separation performance is better for a lower degree of missing array elements. The end result is that the algorithms discussed in this paper can effectively and completely extract all the source signals.

scholarly journals A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Feng Miao ◽  
Rongzhen Zhao
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signals ◽  
Rotary Machinery ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Diagnosis Method ◽  
Machine Health Monitoring ◽  
Machine Health

When the rotary machinery is running, the vibration signals measured with sensors are mixed with all vibration sources and contain very strong noises. It is difficult to separate mixed signals with conventional methods of signal processing, so there are difficulties in machine health monitoring and fault diagnosis. The principle and method of blind source separation were introduced, and it was pointed out that the blind source separation algorithm was invalid in strong pulse noise environment. In these environments, the vibration signals are first denoised with the synchronous cumulative average noise reduction (SCA) method, and the denoised signals were separated with the improved fast independent component analysis (FastICA) algorithm. The results of simulation test and rotor fault experiments demonstrate that the novel method can effectively extract fault features, certifying its superiority in comparison with previous methods. Therefore, it is likely to be useful and practical in the fault detection area, especially under the condition of strong noise and vibration interferences.

scholarly journals Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Lei Chen ◽  
Liyi Zhang ◽  
Yanju Guo ◽  
Yong Huang ◽  
Jingyi Liang
Keyword(s):  
Blind Source Separation ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Source Separation ◽  
Separation Algorithm ◽  
Simulation Experiments ◽  
Bee Colony ◽  
Source Signal ◽  
Source Signals

The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.

scholarly journals Constrained Blind Source Separation by Morphological Characteristics and Its Application in Modal Analysis

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Teng Gong ◽  
Zhousuo Zhang ◽  
Huan Wang
Keyword(s):  
Modal Analysis ◽  
Blind Source Separation ◽  
Prior Information ◽  
Source Separation ◽  
Morphological Characteristics ◽  
Separation Algorithm ◽  
Contrast Function ◽  
Optimization Calculation ◽  
Source Signal ◽  
Source Signals

Semi-blind source separation algorithm is widely concerned for its advantages over classical blind source separation algorithm. However, in practical applications, it is often a difficult problem to design reference signals, which should be closely related to the desired source signals. Therefore the algorithm of constrained blind source separation by morphological characteristics is proposed in this paper, including three steps: the establishment of the enhanced contrast function, the optimization calculation and the extraction of multiple source signals. Firstly, the indexes measuring the morphological characteristics of a source signal are constructed based on the known prior information and introduced into the traditional contrast function to establish an enhanced contrast function, extending the use of prior information. Then, the optimization calculation is accomplished by genetic algorithm, obtaining a single source signal. Finally, the extraction of multiple source signals is realized by cluster analysis. The proposed algorithm is applied to the modal analysis under random excitation. The spectrum symmetry index is constructed and introduced into the kurtosis contrast function to establish the enhanced contrast function, thus realizing the extraction of each signal modal response. The extraction results show the effectiveness and superiority of the algorithm.

scholarly journals Variable Step-Size Method Based on a Reference Separation System for Source Separation

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Pengcheng Xu ◽  
Zhigang Yuan ◽  
Wei Jian ◽  
Wei Zhao
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Variable Step Size ◽  
Separation System ◽  
Step Size ◽  
Original Source ◽  
Variable Step ◽  
Initial Setting ◽  
Free Case ◽  
Source Signals

Traditional variable step-size methods are effective to solve the problem of choosing step-size in adaptive blind source separation process. But the initial setting of learning rate is vital, and the convergence speed is still low. This paper proposes a novel variable step-size method based on reference separation system for online blind source separation. The correlation between the estimated source signals and original source signals increases along with iteration. Therefore, we introduce a reference separation system to approximately estimate the correlation in terms of mean square error (MSE), which is utilized to update the step-size. The use of “minibatches” for the computation of MSE can reduce the complexity of the algorithm to some extent. Moreover, simulations demonstrate that the proposed method exhibits superior convergence and better steady-state performance over the fixed step-size method in the noise-free case, while converging faster than classical variable step-size methods in both stationary and nonstationary environments.

A Novel Blind Source Separation Algorithm for Linear Mixture Signals

2014 ◽  
Vol 599-601 ◽  
pp. 1357-1359
Author(s):  
Wei Hua Liu ◽  
Yun Zhang ◽  
Ying Fu Chen ◽  
Lei Wang ◽  
Jian Cheng Liu
Keyword(s):  
Cost Function ◽  
Blind Source Separation ◽  
Source Separation ◽  
Separation Algorithm ◽  
Mixing Matrix ◽  
Separate Source ◽  
Simulation Results ◽  
The Cost ◽  
Source Signals

A novel blind source separation (BSS) algorithm for linear mixture signals is proposed. It is shown that the property can be used to separate source signals by finding an un-mixing matrix that maximizes the cost function value of separated signals. Simulation results illustrate the efficiency and the good performance of the algorithm.

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