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Journal articles on the topic 'Blind source separation'

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Published: 4 June 2021
Last updated: 20 July 2024

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1

Frikel, Miloud, Victor Barroso, and Joao Xavier. "Blind source separation." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 1101–2. http://dx.doi.org/10.1121/1.425160.

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2

Zhang, Chao Zhu, Ahmed Kareem Abdullah, and Ali Abdullabs Abdullah. "Electroencephalogram-Artifact Extraction Enhancement Based on Artificial Intelligence Technique." Journal of Biomimetics, Biomaterials and Biomedical Engineering 27 (May 2016): 77–91. http://dx.doi.org/10.4028/www.scientific.net/jbbbe.27.77.

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Blind source separation (BSS) is an important technique used to recover isolated independent sources signals from mixtures. This paper proposes two blind artificial intelligent separation algorithms based on hybridization between artificial intelligent techniques with classical blind source separation algorithms to enhance the separation process. Speedy genetic algorithm SGA directly guesses the optimal coefficients of the separating matrix based on candidate initial from classical BSS algorithms also the separation criteria based on minimization of mutual information between the separating independent components. The proposed algorithms are tested by real Electroencephalogram (EEG) data, the experimental results indicate that the algorithms can quickly and effectively get optimum solution to linear blind source separation compared to classical BSS techniques, the proposed works are described by high accuracy and robustness.
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3

Behr, Merle, Chris Holmes, and Axel Munk. "Multiscale blind source separation." Annals of Statistics 46, no. 2 (April 2018): 711–44. http://dx.doi.org/10.1214/17-aos1565.

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4

Bachoc, François, Marc G. Genton, Klaus Nordhausen, Anne Ruiz-Gazen, and Joni Virta. "Spatial blind source separation." Biometrika 107, no. 3 (February 17, 2020): 627–46. http://dx.doi.org/10.1093/biomet/asz079.

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Summary Recently a blind source separation model was suggested for spatial data, along with an estimator based on the simultaneous diagonalization of two scatter matrices. The asymptotic properties of this estimator are derived here, and a new estimator based on the joint diagonalization of more than two scatter matrices is proposed. The asymptotic properties and merits of the novel estimator are verified in simulation studies. A real-data example illustrates application of the method.
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5

Kemiha, Mina, and Abdellah Kacha. "Complex Blind Source Separation." Circuits, Systems, and Signal Processing 36, no. 11 (March 28, 2017): 4670–87. http://dx.doi.org/10.1007/s00034-017-0539-0.

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6

Xu, Jiarui. "Application of blind source separation in sound source separation." Journal of Physics: Conference Series 1345 (November 2019): 032006. http://dx.doi.org/10.1088/1742-6596/1345/3/032006.

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7

Yu, Wen, and Wei Chen. "Smart Noise Jamming Suppression Technique Based on Blind Source Separation." International Journal of Signal Processing Systems 7, no. 1 (March 2019): 14–19. http://dx.doi.org/10.18178/ijsps.7.1.14-19.

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8

Chen, Lingguang, Sean F. Wu, Yong Xu, William D. Lyman, and Gaurav Kapur. "Blind Separation of Heart Sounds." Journal of Theoretical and Computational Acoustics 26, no. 01 (March 2018): 1750035. http://dx.doi.org/10.1142/s2591728517500359.

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This paper presents a theoretical foundation for the newly developed methodology that enables the prediction of blood pressures based on the heart sounds measured directly on the chest of a patient. The key to this methodology is the separation of heart sounds into first heart sound and second heart sound, from which components attributable to four heart valves, i.e.: mitral; tricuspid; aortic; and pulmonary valve-closure sounds are separated. Since human physiology and anatomy can vary among people and are unknown a priori, such separation is called blind source separation. Moreover, the sources locations, their surroundings and boundary conditions are unspecified. Consequently, it is not possible to obtain an exact separation of signals. To circumvent this difficulty, we extend the point source separation method in this paper to an inhomogeneous fluid medium, and further combine it with iteration schemes to search for approximate source locations and signal propagation speed. Once these are accomplished, the signals emitted from individual sources are separated by deconvoluting mixed signals with respect to the identified sources. Both numerical simulation example and experiment have demonstrated that this approach can provide satisfactory source separation results.
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9

Yang, Xiao Yan, Xiong Zhou, and Yi Ke Tang. "A New Method for Adaptive Blind Source Separation Based on the Estimated Number of Dynamic Fault Sources." Applied Mechanics and Materials 233 (November 2012): 211–17. http://dx.doi.org/10.4028/www.scientific.net/amm.233.211.

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In fault diagnosis of large rotating machinery, the number of fault sources may be subject to dynamic changes, which often lead to the failure in accurate estimation of the number of sources and the effective isolation of the fault source. This paper introduced the expansion of the fourth-order cumulant matrices in estimating the dynamic fault source number, plus the relationship between the source signal number and the number of sensors being utilized in the selection of the blind source separation algorithm to achieve adaptive blind source separation. Experiments showed that the source number estimation algorithm could be quite effective in estimating the dynamic number of fault sources, even in the underdetermined condition. This adaptive blind source separation algorithm could then effectively achieve fault diagnosis in respect to the positive-determined, overdetermined and underdetermined blind source separation.
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10

Amari, S. "Superefficiency in blind source separation." IEEE Transactions on Signal Processing 47, no. 4 (April 1999): 936–44. http://dx.doi.org/10.1109/78.752592.

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11

Chenot, Cecile, Jerome Bobin, and Jeremy Rapin. "Robust Sparse Blind Source Separation." IEEE Signal Processing Letters 22, no. 11 (November 2015): 2172–76. http://dx.doi.org/10.1109/lsp.2015.2463232.

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12

liang, Yanxue, and Ichiro Hagiwara. "2003 Source Identification Using Blind Source Separation." Proceedings of the JSME annual meeting 2006.1 (2006): 5–6. http://dx.doi.org/10.1299/jsmemecjo.2006.1.0_5.

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13

Rai, C. S., and Yogesh Singh. "Source distribution models for blind source separation." Neurocomputing 57 (March 2004): 501–5. http://dx.doi.org/10.1016/j.neucom.2004.01.003.

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14

Wang, Chunli, Quanyu Wang, and Yuping Cao. "Blind source separation of indoor mobile voice sources." Mathematical Modelling of Engineering Problems 4, no. 4 (December 30, 2017): 179–83. http://dx.doi.org/10.18280/mmep.040407.

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15

Nadal, J. P., and E. Korutcheva. "Blind source separation of sources with different magnitudes." Computer Physics Communications 121-122 (September 1999): 707. http://dx.doi.org/10.1016/s0010-4655(06)70111-4.

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16

Ding-li, CHU, CHEN Hong, and CHEN Han-yi. "Blind Source Separation based on Whale Optimization Algorithm." MATEC Web of Conferences 173 (2018): 03052. http://dx.doi.org/10.1051/matecconf/201817303052.

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Aiming at the problem of linear instantaneous aliasing in blind source separation, a new method of blind signal separation using whale optimization algorithm is proposed in this paper, which provides a new research idea and method for blind signal separation. The new method adopts the method of independent component analysis, optimizes the objective function by using the whale optimization algorithm, realizes the blind separation of instantaneous aliasing signals, and effectively avoids the problem of complex parameters and slow convergence rate of the particle swarm optimization algorithm. The simulation results show that the performance of whale optimization algorithm is better than that of particle swarm optimization for blind source separation, and it is effective for blind signal separation.
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17

Cheng, Hao, Na Yu, and Jun Liu. "Improved Natural Gradient Algorithms for Multi-Channel Signal Separation." Applied Mechanics and Materials 651-653 (September 2014): 2326–30. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2326.

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In this paper, an Multi-channel blind separation of Direct Sequence Code Division Multiple Access (DS-CDMA) has been introduced. Most of which we assume statistically stationary sources as well as instantaneous mixtures of signals using blind source separation (BSS) algorithms. In practicality, the CDMA sources received are convolute mixing. A more complex blind separation algorithm is required to achieve better source separation. Based on the minimizing the average squared cross-output-channel-correlation, the proposed scheme obtains the better source separation. Simulation results show that the proposed optimal scheme not only achieves superior bit error rate (BER) performance to those of the existing ones, but also provides a guaranteed-convergent solution under different channel conditions.
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18

Qian, Si Chong, and Yang Xiang. "The Relationship between Frequency Domain Blind Source Separation and Frequency Domain Adaptive Beamformer." Applied Mechanics and Materials 490-491 (January 2014): 654–62. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.654.

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As two important methods of array signal processing, blind source separation and beamforming can extract the target signal and suppress interference by using the received information of the array element. In the case of convolution mixture of sources, frequency domain blind source separation and frequency domain adaptive beamforming have similar signal model. To find the relationship between them, comparison between the minimization of the off-diagonal components in the BSS update equation and the minimization of the mean square error in the ABF had been made from the perspective of mathematical expressions, and find that the unmixing matrix of the BSS and the filter coefficients of the ABF converge to the same solution in the mean square error sense under the condition that the two source signals are ideally independent. With MATLAB, the equivalence in the frequency domain have been verified and the causes affecting separation performance have been analyzed, which was achieved by simulating instantaneous and convolution mixtures and separating mixture speech in frequency-domain blind source separation and frequency domain adaptive beamforming way.
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19

Zhao, Zhi Jin, and Zhong Jian Liu. "Mixed NMF Blind Source Separation Algorithm." Applied Mechanics and Materials 596 (July 2014): 169–73. http://dx.doi.org/10.4028/www.scientific.net/amm.596.169.

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The rank one NMF blind source separation algorithm (NMF-R1) was obtained by imposing the sparsity constraint on the fast NMF algorithm based rank one. NMF blind source separation algorithm based on least squares (NMF-LS) was obtained by using pseudo-inverse matrix. NMF-R1 algorithm was superior to the existing blind source separation algorithms based on NMF. NMF-LS algorithm had faster computation speed, but the result of decomposition was not unique. In order to further enhance the signals separated performance, crossover iteration between NMF-R1 and NMF-LS was used to getting the mixing matrix and the signal matrix, and the mixed NMF blind source separation algorithm (NMF-LR) was obtained. Simulation results show that the separation performance of NMF-LR algorithm is better than that of NMF-R1 and NMF-LS, and the computation complexity of NMF-LR algorithm is nearly the same as NMF-R1 algorithm.
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20

Yang, Zuyuan, Yong Xiang, Yue Rong, and Kan Xie. "A Convex Geometry-Based Blind Source Separation Method for Separating Nonnegative Sources." IEEE Transactions on Neural Networks and Learning Systems 26, no. 8 (August 2015): 1635–44. http://dx.doi.org/10.1109/tnnls.2014.2350026.

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21

Keziou, A., H. Fenniri, A. Ghazdali, and E. Moreau. "New blind source separation method of independent/dependent sources." Signal Processing 104 (November 2014): 319–24. http://dx.doi.org/10.1016/j.sigpro.2014.04.017.

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22

Mourad, Nasser, James P. Reilly, and T. Kirubarajan. "Majorization–minimization for blind source separation of sparse sources." Signal Processing 131 (February 2017): 120–33. http://dx.doi.org/10.1016/j.sigpro.2016.08.015.

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23

Nadal, J. P., E. Korutcheva, and F. Aires. "Blind source separation in the presence of weak sources." Neural Networks 13, no. 6 (July 2000): 589–96. http://dx.doi.org/10.1016/s0893-6080(00)00041-1.

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24

Liu, Qiong, Hao Peng, and Haitao Wang. "Intelligent particle blind source separation research." Journal of Computational Methods in Sciences and Engineering 18, no. 2 (May 3, 2018): 319–27. http://dx.doi.org/10.3233/jcm-180791.

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25

Ghazdali, Abdelghani, Abdelmoutalib Metrane, and Amal Ourdou. "Blind Source Separation for Text Mining." Journal of Physics: Conference Series 1743 (January 2021): 012018. http://dx.doi.org/10.1088/1742-6596/1743/1/012018.

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26

Martinez, D., and A. Bray. "Nonlinear blind source separation using kernels." IEEE Transactions on Neural Networks 14, no. 1 (January 2003): 228–35. http://dx.doi.org/10.1109/tnn.2002.806624.

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27

ZHANG, Xianda. "Grading learning for blind source separation." Science in China Series F 46, no. 1 (2003): 31. http://dx.doi.org/10.1360/03yf9003.

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28

Xi-Ren Cao and Ruey-Wen Liu. "General approach to blind source separation." IEEE Transactions on Signal Processing 44, no. 3 (March 1996): 562–71. http://dx.doi.org/10.1109/78.489029.

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29

Shun-Ichi Amari and J. F. Cardoso. "Blind source separation-semiparametric statistical approach." IEEE Transactions on Signal Processing 45, no. 11 (1997): 2692–700. http://dx.doi.org/10.1109/78.650095.

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30

Shi, Z., and C. Zhang. "Energy predictability to blind source separation." Electronics Letters 42, no. 17 (2006): 1006. http://dx.doi.org/10.1049/el:20061456.

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31

Stone, James V. "Blind Source Separation Using Temporal Predictability." Neural Computation 13, no. 7 (July 1, 2001): 1559–74. http://dx.doi.org/10.1162/089976601750265009.

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A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals. It is shown that this property can be used to recover source signals from a set of linear mixtures of those signals by finding an un-mixing matrix that maximizes a measure of temporal predictability for each recovered signal. This matrix is obtained as the solution to a generalized eigenvalue problem; such problems have scaling characteristics of O (N3), where N is the number of signal mixtures. In contrast to independent component analysis, the temporal predictability method requires minimal assumptions regarding the probability density functions of source signals. It is demonstrated that the method can separate signal mixtures in which each mixture is a linear combination of source signals with supergaussian, sub-gaussian, and gaussian probability density functions and on mixtures of voices and music.
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32

Harmeling, Stefan, Andreas Ziehe, Motoaki Kawanabe, and Klaus-Robert Müller. "Kernel-Based Nonlinear Blind Source Separation." Neural Computation 15, no. 5 (May 1, 2003): 1089–124. http://dx.doi.org/10.1162/089976603765202677.

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We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS). It combines complementary research fields: kernel feature spaces and BSS using temporal information. This yields an efficient algorithm for nonlinear BSS with invertible nonlinearity. Key assumptions are that the kernel feature space is chosen rich enough to approximate the nonlinearity and that signals of interest contain temporal information. Both assumptions are fulfilled for a wide set of real-world applications. The algorithm works as follows: First, the data are (implicitly) mapped to a high (possibly infinite)—dimensional kernel feature space. In practice, however, the data form a smaller submanifold in feature space—even smaller than the number of training data points—a fact that has already been used by, for example, reduced set techniques for support vector machines. We propose to adapt to this effective dimension as a preprocessing step and to construct an orthonormal basis of this submanifold. The latter dimension-reduction step is essential for making the subsequent application of BSS methods computationally and numerically tractable. In the reduced space, we use a BSS algorithm that is based on second-order temporal decorrelation. Finally, we propose a selection procedure to obtain the original sources from the extracted nonlinear components automatically. Experiments demonstrate the excellent performance and efficiency of our kTDSEP algorithm for several problems of nonlinear BSS and for more than two sources.
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33

Rasaily, Deepak, Rajesh Mehra, and Naveen Dubey. "Divergence for Blind Audio Source Separation." International Journal of Computer Trends and Technology 28, no. 1 (October 25, 2015): 1–4. http://dx.doi.org/10.14445/22312803/ijctt-v28p101.

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34

Zhou, Yi, and Boling Xu. "Blind source separation in frequency domain." Signal Processing 83, no. 9 (September 2003): 2037–46. http://dx.doi.org/10.1016/s0165-1684(03)00134-8.

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35

Singh, Yogesh, and C. S. Rai. "Blind source separation: a unified approach." Neurocomputing 49, no. 1-4 (December 2002): 435–38. http://dx.doi.org/10.1016/s0925-2312(02)00672-0.

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36

Even, Jani, and Eric Moisan. "Blind source separation using order statistics." Signal Processing 85, no. 9 (September 2005): 1744–58. http://dx.doi.org/10.1016/j.sigpro.2005.04.001.

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37

Kumar, M., and V. E. Jayanthi. "Underdetermined blind source separation using CapsNet." Soft Computing 24, no. 12 (October 15, 2019): 9011–19. http://dx.doi.org/10.1007/s00500-019-04430-4.

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38

Shi, Zhenwei, and Changshui Zhang. "Nonlinear innovation to blind source separation." Neurocomputing 71, no. 1-3 (December 2007): 406–10. http://dx.doi.org/10.1016/j.neucom.2007.08.007.

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39

Grosse-Wentrup, M., and M. Buss. "Subspace identification through blind source separation." IEEE Signal Processing Letters 13, no. 2 (February 2006): 100–103. http://dx.doi.org/10.1109/lsp.2005.861581.

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40

Thi, Hoang-Lan Nguyen, and Christian Jutten. "Blind source separation for convolutive mixtures." Signal Processing 45, no. 2 (August 1995): 209–29. http://dx.doi.org/10.1016/0165-1684(95)00052-f.

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41

Rowlands, Christopher J., and Stephen R. Elliott. "Improved blind-source separation for spectra." Journal of Raman Spectroscopy 42, no. 9 (March 31, 2011): 1761–68. http://dx.doi.org/10.1002/jrs.2936.

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42

Le, Thanh Trung, Karim Abed-Meraim, Philippe Ravier, Olivier Buttelli, and Ales Holobar. "Tensor decomposition meets blind source separation." Signal Processing 221 (August 2024): 109483. http://dx.doi.org/10.1016/j.sigpro.2024.109483.

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43

Todorovic-Zarkula, Slavica, Branimir Todorovic, and Miomir Stankovic. "On-line blind separation of non-stationary signals." Yugoslav Journal of Operations Research 15, no. 1 (2005): 79–95. http://dx.doi.org/10.2298/yjor0501079t.

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This paper addresses the problem of blind separation of non-stationary signals. We introduce an on-line separating algorithm for estimation of independent source signals using the assumption of non-stationary of sources. As a separating model, we apply a self-organizing neural network with lateral connections, and define a contrast function based on correlation of the network outputs. A separating algorithm for adaptation of the network weights is derived using the state-space model of the network dynamics, and the extended Kalman filter. Simulation results obtained in blind separation of artificial and real-world signals from their artificial mixtures have shown that separating algorithm based on the extended Kalman filter outperforms stochastic gradient based algorithm both in convergence speed and estimation accuracy.
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44

Li, Ning, Hai Ting Chen, and Shao Peng Liu. "Rotating Machine Monitoring Based on Blind Source Separation of Correlated Source Signals." Applied Mechanics and Materials 321-324 (June 2013): 1299–302. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1299.

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Blind source separation (BSS) which separate the unknown sources from the observed signals is a new signal processing technique. The most methods for solving this problem rely on assumptions of independence or uncorrelation of source signals at least. However, the observed signal is always interfered by signals with common frequency in the rotating machine, and difficult to be separated by the conventional BSS method. In this paper, it is proved that the source signals with common frequencies are correlative, and the separating error brought by the cross-correlation of the source signals is analyzed. A new separating method for the correlated source signals with frequency overlapping is presented and it is successfully applied to separate the monitoring signals of rotor test stand.
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45

Liu, Jian-Qiang, Da-Zheng Feng, and Wei-Wei Zhang. "Adaptive Improved Natural Gradient Algorithm for Blind Source Separation." Neural Computation 21, no. 3 (March 2009): 872–89. http://dx.doi.org/10.1162/neco.2008.07-07-562.

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We propose an adaptive improved natural gradient algorithm for blind separation of independent sources. First, inspired by the well-known backpropagation algorithm, we incorporate a momentum term into the natural gradient learning process to accelerate the convergence rate and improve the stability. Then an estimation function for the adaptation of the separation model is obtained to adaptively control a step-size parameter and a momentum factor. The proposed natural gradient algorithm with variable step-size parameter and variable momentum factor is therefore particularly well suited to blind source separation in a time-varying environment, such as an abruptly changing mixing matrix or signal power. The expected improvement in the convergence speed, stability, and tracking ability of the proposed algorithm is demonstrated by extensive simulation results in both time-invariant and time-varying environments. The ability of the proposed algorithm to separate extremely weak or badly scaled sources is also verified. In addition, simulation results show that the proposed algorithm is suitable for separating mixtures of many sources (e.g., the number of sources is 10) in the complete case.
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46

Lei, Yan Bin, Zhi Gang Chen, and Hai Ou Liu. "Blind Separation Method for Gearbox Mixed Fault Signals." Applied Mechanics and Materials 86 (August 2011): 180–83. http://dx.doi.org/10.4028/www.scientific.net/amm.86.180.

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A new blind source separation (BSS) algorithm used for separating mixed gearbox signals is proposed in this paper. Firstly, whiten the observed signals, and then diagonalize the second- and higher-order cumulant matrix to get an orthogonal separation matrix. The feasibility of the algorithm is validated through separating the mechanical simulation signals and the gearbox vibration signals. The algorithm can successfully identified the failure source of the gearbox and provides a new method to a gearbox fault.
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47

Tang, Mingyang, and Yafeng Wu. "A Blind Source Separation Method Based on Bounded Component Analysis Optimized by the Improved Beetle Antennae Search." Sensors 23, no. 19 (October 8, 2023): 8325. http://dx.doi.org/10.3390/s23198325.

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Currently, the widely used blind source separation algorithm is typically associated with issues such as a sluggish rate of convergence and unstable accuracy, and it is mostly suitable for the separation of independent source signals. Nevertheless, source signals are not always independent of one another in practical applications. This paper suggests a blind source separation algorithm based on the bounded component analysis of the enhanced Beetle Antennae Search algorithm (BAS). Firstly, the restrictive assumptions of the bounded component analysis method are more relaxed and do not require the signal sources to be independent of each other, broadening the applicability of this blind source separation algorithm. Second, the objective function of bounded component analysis is optimized using the improved Beetle Antennae Search optimization algorithm. A step decay factor is introduced to ensure that the beetle does not miss the optimal point when approaching the target, improving the optimization accuracy. At the same time, since only one beetle is required, the optimization speed is also improved. Finally, simulation experiments show that the algorithm can effectively separate independent and dependent source signals and can be applied to blind source separation of images. Compared to traditional blind source separation algorithms, it has stronger universality and has faster convergence speed and higher accuracy compared to the original independent component analysis algorithm.
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48

Amari, Shun-ichi, Tian-Ping Chen, and Andrzej Cichocki. "Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation." Neural Computation 12, no. 6 (June 1, 2000): 1463–84. http://dx.doi.org/10.1162/089976600300015466.

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Independent component analysis or blind source separation extracts independent signals from their linear mixtures without assuming prior knowledge of their mixing coefficients. It is known that the independent signals in the observed mixtures can be successfully extracted except for their order and scales. In order to resolve the indeterminacy of scales, most learning algorithms impose some constraints on the magnitudes of the recovered signals. However, when the source signals are nonstationary and their average magnitudes change rapidly, the constraints force a rapid change in the magnitude of the separating matrix. This is the case with most applications (e.g., speech sounds, electroencephalogram signals). It is known that this causes numerical instability in some cases. In order to resolve this difficulty, this article introduces new nonholonomic constraints in the learning algorithm. This is motivated by the geometrical consideration that the directions of change in the separating matrix should be orthogonal to the equivalence class of separating matrices due to the scaling indeterminacy. These constraints are proved to be nonholonomic, so that the proposed algorithm is able to adapt to rapid or intermittent changes in the magnitudes of the source signals. The proposed algorithm works well even when the number of the sources is overestimated, whereas the existent algorithms do not (assuming the sensor noise is negligibly small), because they amplify the null components not included in the sources. Computer simulations confirm this desirable property.
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49

Yu, Yang, and Xiang Zhou. "Study on Corrosion Acoustic Emission Separation Based on Blind Source Separation." Advanced Materials Research 503-504 (April 2012): 1597–600. http://dx.doi.org/10.4028/www.scientific.net/amr.503-504.1597.

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When corrosion signals of tank bottom is detected by online method, it is essential to identify corrosion signals of different corrosion pots. It is a new method based on blind source separation. Blind source separation has produced many arithetics, among which entropy maximization is more mature. The aim of this paper is to separate corrosion signals by using entropy maximization arithmetic. Furthermore, the separation of linear mixed acoustic emission signals is achieved through simulation. The results indicate that blind source separation is an effective method for the separation of corrosion signals.
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50

Roan, Michael J., and Josh Erling. "Blind source separation and blind deconvolution in experimental acoustics." Journal of the Acoustical Society of America 108, no. 5 (November 2000): 2628–29. http://dx.doi.org/10.1121/1.4743787.

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