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Journal articles on the topic 'Blind source separation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

x, Ekta, and Shelej Khera. "Acoustic Echo Canceller with Blind Source Separation." International Journal of Scientific Engineering and Research 3, no. 6 (2015): 83–85. https://doi.org/10.70729/ijser15265.

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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 in

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3

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

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4

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

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5

Bachoc, François, Marc G. Genton, Klaus Nordhausen, Anne Ruiz-Gazen, and Joni Virta. "Spatial blind source separation." Biometrika 107, no. 3 (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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6

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

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7

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 numbe

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8

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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9

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

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10

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 (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 sour

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11

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

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12

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

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13

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

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14

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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15

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. Th

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16

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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17

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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18

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

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19

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 mathematic

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20

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 matr

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21

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 (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 t

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22

Li, Zhi Nong, Fen Zhang, Xu Ping He, and Yao Xian Xiao. "Application of the Blind Source Separation Based on Time-Frequency Analysis in Mechanical Fault Diagnosis." Advanced Materials Research 945-949 (June 2014): 1054–62. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1054.

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Blind source separation provides a new method for the separation of mechanical sources under high level background noise, as well as the diagnosis of the compound fault. At present, the blind source separation has been successfully applied to the mecanical fault diagnosis. But the traditional mechanical source separation methods are restricted to non-gauss, stationary and mutually independent source signals. However, the mechanical fault signals do not suffice to these conditions, and generally exhibit non-stationarity and non-independence. For the non-stationary signal, its spectral feature i

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23

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 (2015): 1635–44. http://dx.doi.org/10.1109/tnnls.2014.2350026.

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24

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 corrosi

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25

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 arti

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26

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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27

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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28

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

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29

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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30

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

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31

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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32

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

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33

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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34

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

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35

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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36

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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37

Stone, James V. "Blind Source Separation Using Temporal Predictability." Neural Computation 13, no. 7 (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 m

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38

Harmeling, Stefan, Andreas Ziehe, Motoaki Kawanabe, and Klaus-Robert Müller. "Kernel-Based Nonlinear Blind Source Separation." Neural Computation 15, no. 5 (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 (possibl

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39

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

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40

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

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41

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

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42

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

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43

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

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44

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

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45

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

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46

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

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47

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

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48

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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49

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 an

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50

Liu, Jian-Qiang, Da-Zheng Feng, and Wei-Wei Zhang. "Adaptive Improved Natural Gradient Algorithm for Blind Source Separation." Neural Computation 21, no. 3 (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

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