Journal articles: 'Blind Source Separation (BSS)'

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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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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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Ye, Ji-Min, Xiao-Long Zhu, and Xian-Da Zhang. "Adaptive Blind Separation with an Unknown Number of Sources." Neural Computation 16, no. 8 (August 1, 2004): 1641–60. http://dx.doi.org/10.1162/089976604774201622.

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The blind source separation (BSS) problem with an unknown number of sources is an important practical issue that is usually skipped by assuming that the source number n is known and equal to the number m of sensors. This letter studies the general BSS problem satisfying m ≥ n. First, it is shown that the mutual information of outputs of the separation network is a cost function for BSS, provided that the mixing matrix is of full column rank and the m×m separating matrix is nonsingular. The mutual information reaches its local minima at the separation points, where the m outputs consist of n desired source signals and m−n redundant signals. Second, it is proved that the natural gradient algorithm proposed primarily for complete BSS (m n) can be generalized to deal with the overdetermined BSS problem (m>n), but it would diverge inevitably due to lack of a stationary point. To overcome this shortcoming, we present a modified algorithm, which can perform BSS steadily and provide the desired source signals at specified channels if some matrix is designed properly. Finally, the validity of the proposed algorithm is confirmed by computer simulations on artificially synthesized data.

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Zi, Jiali, Danju Lv, Jiang Liu, Xin Huang, Wang Yao, Mingyuan Gao, Rui Xi, and Yan Zhang. "Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation." Sensors 22, no. 1 (December 24, 2021): 118. http://dx.doi.org/10.3390/s22010118.

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In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.

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Yin, Hong Wei, Guo Lin Li, and Cui Hua Lu. "Step Adaptive Normalization Blind Source Separation Algorithm." Advanced Materials Research 1049-1050 (October 2014): 1407–12. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1407.

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An algorithm of step adaptive normalization BSS(SAN-BSS) is proposed to solve the problem that the traditional switching BSS algorithms are sensitive to the types and the number of the source signals. The proposed algorithm improves the original ones’ stability by making use of the normalization mechanism to modify the cost functions, and realizes the adaptive updating of the step size by combining the signals’ separation process with the summation of the edge negentropy. The simulation results show that when the number of the source signals improves or the types of the signals change, the proposed algorithm can keep good separation effect. Compared with the original ones, the separation accuracy of our proposed algorithm improved 98%, and the number of iterations reduced nearly 60%, which improved the stability and the separation speed of the algorithm greatly.

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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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Gao, Tao, and Jincan Li. "The Research and Simulation of Blind Source Separation Algorithm." International Journal of Advanced Pervasive and Ubiquitous Computing 8, no. 3 (July 2016): 1–36. http://dx.doi.org/10.4018/ijapuc.2016070101.

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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.

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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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Theis, Fabian J. "A New Concept for Separability Problems in Blind Source Separation." Neural Computation 16, no. 9 (September 1, 2004): 1827–50. http://dx.doi.org/10.1162/0899766041336404.

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The goal of blind source separation (BSS) lies in recovering the original independent sources of a mixed random vector without knowing the mixing structure. A key ingredient for performing BSS successfully is to know the indeterminacies of the problem—that is, to know how the separating model relates to the original mixing model (separability). For linear BSS, Comon (1994) showed using the Darmois-Skitovitch theorem that the linear mixing matrix can be found except for permutation and scaling. In this work, a much simpler, direct proof for linear separability is given. The idea is based on the fact that a random vector is independent if and only if the Hessian of its logarithmic density (resp. characteristic function) is diagonal everywhere. This property is then exploited to propose a new algorithm for performing BSS. Furthermore, first ideas of how to generalize separability results based on Hessian diagonalization to more complicated nonlinear models are studied in the setting of postnonlinear BSS.

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MÜLLER, KLAUS-ROBERT, RICARDO VIGÁRIO, FRANK MEINECKE, and ANDREAS ZIEHE. "BLIND SOURCE SEPARATION TECHNIQUES FOR DECOMPOSING EVENT-RELATED BRAIN SIGNALS." International Journal of Bifurcation and Chaos 14, no. 02 (February 2004): 773–91. http://dx.doi.org/10.1142/s0218127404009466.

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Recently blind source separation (BSS) methods have been highly successful when applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of event-related MEG measurements. In a first experiment we apply BSS to artifact identification of raw MEG data and discuss how the quality of the resulting independent component projections can be evaluated. The second part of our study considers averaged data of event-related magnetic fields. Here, it is particularly important to monitor and thus avoid possible overfitting due to limited sample size. A stability assessment of the BSS decomposition allows to solve this task and an additional grouping of the BSS components reveals interesting structure, that could ultimately be used for gaining a better physiological modeling of the data.

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Li, Hong Kun, Hong Yi Liu, and Chang Bo He. "Blind Source Separation for Under-Determined Mixtures Based on Time-Frequency Analysis." Key Engineering Materials 693 (May 2016): 1350–56. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1350.

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Blind source separation (BSS) is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration sources. The traditional BSS algorithm is applicable to the number of observed signals is no less to the source signals. But BSS performance is limit for the under-determined condition that the number of observed signals is less than source signals. In this research, we provide an under-determined BSS method based on the advantage of time-frequency analysis and empirical mode decomposition (EMD). It is suitable for weak feature extraction and pattern recognition. Firstly, vibration signal is decomposed by using EMD. The number of source signals are estimated and the optimal observed signals are selected according to the EMD. Then, the vibration signal and the optimal observed signals are used to construct the multi-channel observed signals. In the end, BSS based on time-frequency analysis are used to the constructed signals. Gearbox signals are used to verify the effectiveness of this method.

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Putra, Angga Pramana, and I. Gede Arta Wibawa. "Fast Independent Component Analysis (FastICA) in Separating Vocals and Instruments in the Art of Geguntangan." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 3 (January 25, 2020): 219. http://dx.doi.org/10.24843/jlk.2020.v08.i03.p02.

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Geguntangan is pesantian in religious ceremonies in Bali accompanied by gamelan music. The human sense of hearing tends to have limitations, which causes not all vocals mixed with gamelan to be heard clearly. Therefore we need a system that can be used to separate vocals with gamelan in the geguntangan. Separation of sound sources is categorized as Blind Source Separation (BSS) or also called Blind Signal Separation, which means an unknown source. The algorithm used to handle BSS is the Fast Independent Component Analysis (FastICA) algorithm with a focus on separating the sound signal in a wav-format sound file. FastICA algorithm is used for the sound separation process with the value parameter used is Mean Square Error (MSE). From the simulation results show the results of MSE calculations using the mixing matrix [0.3816, 0.8678], [0.8534, -0.5853] obtained the results for the FastICA method, the MSE value is 3.60 x 10-5 for the vocal and 1.71 x 10-6 for the instrument.

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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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Chen, Jiajia, Haijian Zhang, and Siyu Sun. "Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation." Electronics 13, no. 7 (March 26, 2024): 1227. http://dx.doi.org/10.3390/electronics13071227.

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This paper explores the important role of blind source separation (BSS) techniques in separating M mixtures including N sources using a dual-sensor array, i.e., M=2, and proposes an efficient two-stage underdetermined BSS (UBSS) algorithm to estimate the mixing matrix and achieve source recovery by exploiting time–frequency (TF) sparsity. First, we design a mixing matrix estimation method by precisely identifying high clustering property single-source TF points (HCP-SSPs) with a spatial vector dictionary based on the principle of matching pursuit (MP). Second, the problem of source recovery in the TF domain is reformulated as an equivalent sparse recovery model with a relaxed sparse condition, i.e., enabling the number of active sources at each auto-source TF point (ASP) to be larger than M. This sparse recovery model relies on the sparsity of an ASP matrix formed by stacking a set of predefined spatial TF vectors; current sparse recovery tools could be utilized to reconstruct N>2 sources. Experimental results are provided to demonstrate the effectiveness of the proposed UBSS algorithm with an easily configured two-sensor array.

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DING, SHUXUE, JIE HUANG, and DAMING WEI. "REAL-TIME BLIND SOURCE SEPARATION OF ACOUSTIC SIGNALS WITH A RECURSIVE APPROACH." International Journal of Computational Intelligence and Applications 04, no. 02 (June 2004): 193–206. http://dx.doi.org/10.1142/s1469026804001252.

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We propose an approach for real-time blind source separation (BSS), in which the observations are linear convolutive mixtures of statistically independent acoustic sources. A recursive least square (RLS)-like strategy is devised for real-time BSS processing. A normal equation is further introduced as an expression between the separation matrix and the correlation matrix of observations. We recursively estimate the correlation matrix and explicitly, rather than stochastically, solve the normal equation to obtain the separation matrix. As an example of application, the approach has been applied to a BSS problem where the separation criterion is based on the second-order statistics and the non-stationarity of signals in the frequency domain. In this way, we realise a novel BSS algorithm, called exponentially weighted recursive BSS algorithm. The simulation and experimental results showed an improved separation and a superior convergence rate of the proposed algorithm over that of the gradient algorithm. Moreover, this algorithm can converge to a much lower cost value than that of the gradient algorithm.

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Isomura, Takuya, and Taro Toyoizumi. "On the Achievability of Blind Source Separation for High-Dimensional Nonlinear Source Mixtures." Neural Computation 33, no. 6 (May 13, 2021): 1433–68. http://dx.doi.org/10.1162/neco_a_01378.

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For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains unclear why these linear methods work well with real-world data that involve nonlinear source mixtures. This work theoretically validates that a cascade of linear PCA and ICA can solve a nonlinear BSS problem accurately—when the sensory inputs are generated from hidden sources via nonlinear mappings with sufficient dimensionality. Our proposed theorem, termed the asymptotic linearization theorem, theoretically guarantees that applying linear PCA to the inputs can reliably extract a subspace spanned by the linear projections from every hidden source as the major components—and thus projecting the inputs onto their major eigenspace can effectively recover a linear transformation of the hidden sources. Then subsequent application of linear ICA can separate all the true independent hidden sources accurately. Zero-element-wise-error nonlinear BSS is asymptotically attained when the source dimensionality is large and the input dimensionality is sufficiently larger than the source dimensionality. Our proposed theorem is validated analytically and numerically. Moreover, the same computation can be performed by using Hebbian-like plasticity rules, implying the biological plausibility of this nonlinear BSS strategy. Our results highlight the utility of linear PCA and ICA for accurately and reliably recovering nonlinearly mixed sources and suggest the importance of employing sensors with sufficient dimensionality to identify true hidden sources of real-world data.

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Zhang, Hong Bin, and Peng Fei Xu. "Convolutive Blind Source Separation Based on Wavelet De-Noising." Advanced Materials Research 756-759 (September 2013): 3356–61. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3356.

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The paper discusses the time-domain blind seperation applied to communication signals, using an ICA algorithm EFICA together with a wavelet de-noising processing method. In the Blind source separation system, regardless of the mixed signals and separated signals, noise pollution occurs frequently, it increases the complexity of BSS and the difficulty of dealing with the aftermath. So an automatic method of and wavelet de-noising processing is proposed finally. It yields good results in the experiment and improves the performance of BSS system.

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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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Li, Yuanqing, Andrzej Cichocki, and Shun-ichi Amari. "Analysis of Sparse Representation and Blind Source Separation." Neural Computation 16, no. 6 (June 1, 2004): 1193–234. http://dx.doi.org/10.1162/089976604773717586.

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In this letter, we analyze a two-stage cluster-then-l1-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l1 norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l1—norm solution and the l0—norm solution is also analyzed according to a probabilistic framework. If the obtained l1—norm solution is sufficiently sparse, then it is equal to the l0—norm solution with a high probability. Furthermore, the l1—norm solution is robust to noise, but the l0—norm solution is not, showing that the l1—norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed

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Xu, Dong Hui, Hong Guang Ma, Wen Pu Guo, and Dong Dong Yang. "Algorithm of Adaptive Online Blind Signal Separation Based on Matrix Separation." Applied Mechanics and Materials 380-384 (August 2013): 3978–81. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3978.

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For the studied the over-determined blind source separation algorithm and its application in radar signal sorting. The reason that natural gradient algorithm of over-determined BSS can not stably converge eventually is analyzed. Aimed at the problem that the exiting methods are analyzed and researched. On-line BSS algorithm with adaptive step length based on separating matrix is presented to implement the optimum combination between the convergence speed and the steady-state error. At the same time, the algorithm can achieve a better separating result when the signal is randomly reduced or increased. The simulation result verifies the convergence stability and the separating effectivity of the two improved algorithms.

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Celik, Husamettin, and Nurhan Karaboga. "Blind Source Separation with Strength Pareto Evolutionary Algorithm 2 (SPEA2) Using Discrete Wavelet Transform." Electronics 12, no. 21 (October 24, 2023): 4383. http://dx.doi.org/10.3390/electronics12214383.

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This paper presents a new method for separating the mixed audio signals of simultaneous speakers using Blind Source Separation (BSS). The separation of mixed signals is an important issue today. In order to obtain more efficient and superior source estimation performance, a new algorithm that solves the BSS problem with Multi-Objective Optimization (MOO) methods was developed in this study. In this direction, we tested the application of two methods. Firstly, the Discrete Wavelet Transform (DWT) was used to eliminate the limited aspects of the traditional methods used in BSS and the small coefficients in the signals. Afterwards, the BSS process was optimized with the multi-purpose Strength Pareto Evolutionary Algorithm 2 (SPEA2). Secondly, the Minkowski distance method was proposed for distance measurement by using density information in the discrimination of individuals with raw fitness values for the concept of Pareto dominance. With this proposed method, the originals (original source signals) were estimated by separating the randomly mixed male and two female speech signals. Simulation and experimental results proved that the efficiency and performance of the proposed method can effectively solve BSS problems. In addition, the Pareto front approximation performance of this method also confirmed that it is superior in the Inverted Generational Distance (IGD) indicator.

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Ourdou, A., A. Ghazdali, A. Laghrib, and A. Metrane. "Robust approach for blind separation of noisy mixtures of independent and dependent sources." Mathematical Modeling and Computing 8, no. 4 (2021): 761–69. http://dx.doi.org/10.23939/mmc2021.04.761.

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In this paper, a new Blind Source Separation (BSS) method that handles mixtures of noisy independent/dependent sources is introduced. We achieve that by minimizing a criterion that fuses a separating part, based on Kullback–Leibler divergence for either dependent or independent sources, with a regularization part that employs the bilateral total variation (BTV) for the purpose of denoising the observations. The proposed algorithm utilizes a primal-dual algorithm to remove the noise, while a gradient descent method is implemented to retrieve the signal sources. Our algorithm has shown its effectiveness and efficiency and also surpassed the standard existing BSS algorithms.

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Zhang, Zhiwei, Hongyuan Gao, Jingya Ma, Shihao Wang, and Helin Sun. "Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise." Mathematical Problems in Engineering 2021 (July 28, 2021): 1–17. http://dx.doi.org/10.1155/2021/1496156.

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In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.

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Wang, Xiao Wei, Lin Suo Shi, Shuang Chen, Hui Li, and Wei Zhang. "Gear Faults Diagnosis Based on Nonlinear Blind Source Separation." Advanced Materials Research 548 (July 2012): 507–10. http://dx.doi.org/10.4028/www.scientific.net/amr.548.507.

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The nonlinear blind source separation is a practical and effective method in processing mechanical vibration signal, but it has the limitation which learning rate is fixed. It will take a long time for iterative parameters to get convergence. In this paper, a variable rate nonlinear BSS is proposed. The learning rate of the algorithm is adjusted based on iterative error in the different stopping iterating time and inverse proportion. The proposed algorithm increasing the efficiency of the nonlinear BSS and de-noising the vibration signals. Experiment on gears shows that the signal gained by the method more impersonality represents the gear condition

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Li, Ning, and Hai Ting Chen. "Blind Extraction of Correlated Fault Sources Based on Constrained Non-Negative Matrix Factorization." Applied Mechanics and Materials 226-228 (November 2012): 760–64. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.760.

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Blind source separation (BSS) has been successfully used to extract undetected fault vibration sources from mixed observation signals by assuming that each unknown vibration source is mutually independent. However, conventional BSS algorithms cannot address the situation in which the fault source could be partially dependent on or correlated to other sources. For this, a new matrix decomposition method, called Non-negative Matrix Factorization (NMF), is introduced to separate these partially correlated signals. In this paper, the observed temporal signals are transformed into the frequency domain to satisfy the non-negative limit of NMF. The constraint of the least correlation between the separated sources is added into the cost function of NMF to enhance the stability of NMF, and the constrained non-negative matrix factorization (CNMF) is proposed. The simulation results show that the separation performance of CNMF is superior to the common BSS algorithms and the experiment result verifies the practical performance of CNMF.

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Zhou, Hao, Chang Zheng Chen, Xian Ming Sun, and Huan Liu. "Research on Blind Source Separation Algorithm Based on Particle Swarm Optimization." Advanced Materials Research 989-994 (July 2014): 1566–69. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1566.

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Blind source separation (BSS) is a technique for recovering a set of source signals without priori information on the transformation matrix or the probability distributions of the source signals. In the previous works of BSS, the choice of the learning rate would reflect a trade-off between the stability and the speed of convergence. In this paper, a particle swarm optimization (PSO)-based learning rate adjustment method is proposed for BSS. In the simulations, three source signals are mixed and separated and the results are compared with natural gradient algorithm. The proposed approach exhibits rapid convergence, and produces more efficient and more stable independent component analysis algorithms than other related approaches.

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Liu, Guo Hua, Zeng Shan Yin, Zheng Wei Wang, Xiao Song Yao, and Wen Zhe Yang. "Research on Blind Source Separation with Noise Based on Multi Factor Analysis." Applied Mechanics and Materials 644-650 (September 2014): 3947–50. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3947.

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The pre-processing method of blind source separation based on multifactor analysis is proposed to solve the blind source with noise. Firstly, the shortcomings of existing methods of blind source separation are point out after analyzing their principles. The multifactor analysis is introduced in blind source separation and the maximum likelihood estimate based on expectation maximum is used to estimate the common factor and random error. Finally the FastICA algorithm is used to separate BSS result. The validity and the advantage of this method are illustrated by an example.

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Li, Xiu Kun, and Qi Yong Wang. "Blind Separability of Reverberation and Target Echo Based on Spatial Correlation." Applied Mechanics and Materials 128-129 (October 2011): 538–43. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.538.

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The detection of buried targets in shallow water is a tough task in the presence of sea-bottom reverberation. Because both target echo and reverberation are caused by the transmitted signal, they are mixed together in both time domain and frequency domain, which makes traditional signal processing methods inefficient. Blind Source Separation (BSS) is expected to isolate the reverberation from the target echo. However, the feasibility should be proved before separation. In this paper, a method based on spatial correlation is proposed to determine whether reverberation and target echo can be separated as different sources. Then, considering the nonstationarity of the reverberation, SONS (Second Order Nonstationary Source Separation) is applied to separate the original received signals. The sea experiment result shows that BSS is not only feasible but also valid to separate target echo and reverberation, and the target echo after BSS is of higher SRR which makes further process more credible.

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Ghosh, Anushreya, Annan Dong, Alexander Haimovich, Osvaldo Simeone, and Jason Dabin. "Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels." Entropy 25, no. 9 (September 3, 2023): 1292. http://dx.doi.org/10.3390/e25091292.

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This paper studies blind source separation (BSS) for frequency hopping (FH) sources. These radio frequency (RF) signals are observed by a uniform linear array (ULA) over (i) line-of-sight (LOS), (ii) single-cluster, and (iii) multiple-cluster Spatial Channel Model (SCM) settings. The sources are stationary, spatially sparse, and their activity is intermittent and assumed to follow a hidden Markov model (HMM). BSS is achieved by leveraging direction of arrival (DOA) information through an FH estimation stage, a DOA estimation stage, and a pairing stage with the latter associating FH patterns with physical sources via their estimated DOAs. Current methods in the literature do not perform the association of multiple frequency hops to the sources they are transmitted from. We bridge this gap by pairing the FH estimates with DOA estimates and labeling signals to their sources, irrespective of their hopped frequencies. A state filtering technique, referred to as hidden state filtering (HSF), is developed to refine DOA estimates for sources that follow a HMM. Numerical results demonstrate that the proposed approach is capable of separating multiple intermittent FH sources.

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Luo, Zhongqiang, Wei Zhang, Lidong Zhu, and Chengjie Li. "Minimum BER Criterion Based Robust Blind Separation for MIMO Systems." Infocommunications journal, no. 1 (2019): 38–44. http://dx.doi.org/10.36244/icj.2019.1.5.

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In this paper, a robust blind source separation (BSS) algorithm is investigated based on a new cost function for noise suppression. This new cost function is established according to the criterion of minimum bit error rate (BER) incorporated into maximum likelihood (ML) principle based independent component analysis (ICA). With the help of natural gradient search, the blind separation work is carried out through optimizing this constructed cost function. Simulation results and analysis corroborate that the proposed blind separation algorithm can realize better performance in speed of convergence and separation accuracy as opposed to the conventional ML-based BSS.

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Karhunen, J., A. Cichocki, W. Kasprzak, and P. Pajunen. "On Neural Blind Separation with Noise Suppression and Redundancy Reduction." International Journal of Neural Systems 08, no. 02 (April 1997): 219–37. http://dx.doi.org/10.1142/s0129065797000239.

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Noise is an unavoidable factor in real sensor signals. We study how additive and convolutive noise can be reduced or even eliminated in the blind source separation (BSS) problem. Particular attention is paid to cases in which the number of sensors is larger than the number of sources. We propose various methods and associated adaptive learning algorithms for such an extended BSS problem. Performance and validity of the proposed approaches are demonstrated by extensive computer simulations.

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Wei, Shuang, De Fu Jiang, and Yang Gao. "A Diversity-Guided Particle Swarm Optimization Method for Blind Source Separation." Applied Mechanics and Materials 513-517 (February 2014): 876–80. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.876.

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This paper presents a diversity-guided Particle swarm optimization (PSO) algorithm to resolve the Blind source separation (BSS) problem. Because the independent component analysis (ICA) approach, a popular method for the BSS problem, has a shortcoming of premature convergence during the optimization process, the proposed PSO algorithm aims to improve this issue by using the diversity calculation to avoid trapping in the local optima. In the experiment, the performance of the proposed PSO algorithm for the BSS problem has been investigated and the results are compared with the conventional PSO algorithm. It shows that the proposed PSO algorithm outperforms the conventional PSO algorithm.

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NAIK, GANESH R., and DINESH K. KUMAR. "SUBTLE ELECTROMYOGRAPHIC PATTERN RECOGNITION FOR FINGER MOVEMENTS: A PILOT STUDY USING BSS TECHNIQUES." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1250078. http://dx.doi.org/10.1142/s0219519412005009.

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In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax).

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Huang, Jingwen, and Jianshan Sun. "Sampling Adaptive Learning Algorithm for Mobile Blind Source Separation." Wireless Communications and Mobile Computing 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/5048419.

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Learning rate plays an important role in separating a set of mixed signals through the training of an unmixing matrix, to recover an approximation of the source signals in blind source separation (BSS). To improve the algorithm in speed and exactness, a sampling adaptive learning algorithm is proposed to calculate the adaptive learning rate in a sampling way. The connection for the sampled optimal points is described through a smoothing equation. The simulation result shows that the performance of the proposed algorithm has similar Mean Square Error (MSE) to that of adaptive learning algorithm but is less time consuming.

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Guo, Jie, An Quan Wei, and Lei Tang. "Blind Complex Source Separation Based on Cyclostationary Statistics." Applied Mechanics and Materials 519-520 (February 2014): 1051–56. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1051.

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This paper analyzed a blind source separation algorithm based on cyclic frequency of complex signals. Under the blind source separation model, we firstly gave several useful assumptions. Then we discussed the derivation of the BSS algorithm, including the complex signals and the normalization situation. Later, we analyzed the complex WCW-CS algorithm, which was compared with NGA, NEASI and NGA-CS algorithms. Simulation results show that the complex WCW-CS algorithm has the best convergence and separation performance. It can also effectively separate mixed image signals, whose performance was better than NGA algorithm.

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Truong, Quang Tan, Huy Quang Tran, and Phuong Huu Nguyen. "BLIND SOURCE SEPARATION (BSS) APPLIED TO SOUND IN VARIOUS CONDITIONS." Science and Technology Development Journal 14, no. 4 (December 30, 2011): 34–42. http://dx.doi.org/10.32508/stdj.v14i4.2034.

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Our ears often simultaneously receive various sound sources (speech, music, noise . . .), but we can still listen to the intended sound. A system of speech recognition must be able to achieve the same intelligent level. The problem is that we receive many mixed (combined) signals from many different source signals, and would like to recover them separately. This is the problem of Blind Source Separation (BSS). In the last decade or so a method has been developed to solve the above problem effectively, that is the Independent Component Analysis (ICA). There are many ICA algorithms for different applications. This report describes our application to sound separation when there are more sources than mixtures (underdetermined case). The results were quite good.

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Palagan, C. Anna, and K. Parimala Geetha. "Implementation of Frequency Domain Approach Using Instantaneous Mixing Auto Recursive for Separation of Speech Signals." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 6576–84. http://dx.doi.org/10.1166/jctn.2016.5604.

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In the present work a novel algorithmic rule by taking the speech from two different microphones and separate these speeches by prediction of separating speech mixtures that is predicated on separation matrices is planned. In multi-talker applications so as to boost individual speech sources from their mixtures is done by Blind source Separation (BSS) ways. From the previous published works of separation of speech signals, the main disadvantage is that the incidence of distortion present within the signal that affects separated signal with loud musical noise. The idea for speech separation in standard BSS ways is simply one sound source in a single room. The proposed methodology uses as a network that has the parameters of the IMAR model for the separation matrices over the complete frequency vary. An attempt has been made to estimate the best values of the IMAR model parameters, ΦW and ΦG by suggests that of the maximum-likelihood estimation methodology. Based on the values of these parameters, the source spectral part vectors are estimated. The entire set of TIMIT corpus is employed for speech materials in evolution results. The Signal to Interference magnitude Relation (SIR) improves by a median of 6 dB sound unit over a frequency domain BSS approach.

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Vullings, Rik, and Massimo Mischi. "Probabilistic Source Separation for Robust Fetal Electrocardiography." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/109756.

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Blind source separation (BSS) techniques are widely used to extract signals of interest from a mixture with other signals, such as extracting fetal electrocardiogram (ECG) signals from noninvasive recordings on the maternal abdomen. These BSS techniques, however, typically lack possibilities to incorporate any prior knowledge on the mixing of the source signals. Particularly for fetal ECG signals, knowledge on the mixing is available based on the origin and propagation properties of these signals. In this paper, a novel source separation method is developed that combines the strengths and accuracy of BSS techniques with the robustness of an underlying physiological model of the fetal ECG. The method is developed within a probabilistic framework and yields an iterative convergence of the separation matrix towards a maximum a posteriori estimation, where in each iteration the latest estimate of the separation matrix is corrected towards a tradeoff between the BSS technique and the physiological model. The method is evaluated by comparing its performance with that of FastICA on both simulated and real multichannel fetal ECG recordings, demonstrating that the developed method outperforms FastICA in extracting the fetal ECG source signals.

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Huang, Xiangdong, Xukang Jin, and Haipeng Fu. "Short-Sampled Blind Source Separation of Rotating Machinery Signals Based on Spectrum Correction." Shock and Vibration 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/9564938.

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Nowadays, the existing blind source separation (BSS) algorithms in rotating machinery fault diagnosis can hardly meet the demand of fast response, high stability, and low complexity simultaneously. Therefore, this paper proposes a spectrum correction based BSS algorithm. Through the incorporation of FFT, spectrum correction, a screen procedure (consisting of frequency merging, candidate pattern selection, and single-source-component recognition), modifiedk-means based source number estimation, and mixing matrix estimation, the proposed BSS algorithm can accurately achieve harmonics sensing on field rotating machinery faults in case of short-sampled observations. Both numerical simulation and practical experiment verify the proposed BSS algorithm’s superiority in the recovery quality, stability to insufficient samples, and efficiency over the existing ICA-based methods. Besides rotating machinery fault diagnosis, the proposed BSS algorithm also possesses a vast potential in other harmonics-related application fields.

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Xie, Shengli, Zhaoshui He, and Yuli Fu. "A Note on Stone's Conjecture of Blind Signal Separation." Neural Computation 17, no. 2 (February 1, 2005): 321–30. http://dx.doi.org/10.1162/0899766053011492.

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Stone's method is one of the novel approaches to the blind source separation (BSS) problem and is based on Stone's conjecture. However, this conjecture has not been proved. We present a simple simulation to demonstrate that Stone's conjecture is incorrect. We then modify Stone's conjecture and prove this modified conjecture as a theorem, which can be used a basis for BSS algorithms.

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41

Yu, Gang. "An Underdetermined Blind Source Separation Method with Application to Modal Identification." Shock and Vibration 2019 (October 8, 2019): 1–15. http://dx.doi.org/10.1155/2019/1637163.

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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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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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Khan, Junaid Bahadar, Tariqullah Jan, Ruhul Amin Khalil, Nasir Saeed, and Muhannad Almutiry. "An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture." Applied Sciences 11, no. 13 (June 27, 2021): 5968. http://dx.doi.org/10.3390/app11135968.

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This paper proposes a novel efficient multistage algorithm to extract source speech signals from a noisy convolutive mixture. The proposed approach comprises two stages named Blind Source Separation (BSS) and de-noising. A hybrid source prior model separates the source signals from the noisy reverberant mixture in the BSS stage. Moreover, we model the low- and high-energy components by generalized multivariate Gaussian and super-Gaussian models, respectively. We use Minimum Mean Square Error (MMSE) to reduce noise in the noisy convolutive mixture signal in the de-noising stage. Furthermore, the two proposed models investigate the performance gain. In the first model, the speech signal is separated from the observed noisy convolutive mixture in the BSS stage, followed by suppression of noise in the estimated source signals in the de-noising module. In the second approach, the noise is reduced using the MMSE filtering technique in the received noisy convolutive mixture at the de-noising stage, followed by separation of source signals from the de-noised reverberant mixture at the BSS stage. We evaluate the performance of the proposed scheme in terms of signal-to-distortion ratio (SDR) with respect to other well-known multistage BSS methods. The results show the superior performance of the proposed algorithm over the other state-of-the-art methods.

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Ding, Hua, Yiliang Wang, Zhaojian Yang, and Olivia Pfeiffer. "Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis." Applied Sciences 9, no. 9 (May 6, 2019): 1852. http://dx.doi.org/10.3390/app9091852.

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Mining machines are strongly nonlinear systems, and their transmission vibration signals are nonlinear mixtures of different kinds of vibration sources. In addition, vibration signals measured by the accelerometer are contaminated by noise. As a result, it is inefficient and ineffective for the blind source separation (BSS) algorithm to separate the critical independent sources associated with the transmission fault vibrations. For this reason, a new method based on wavelet de-noising and nonlinear independent component analysis (ICA) is presented in this paper to tackle the nonlinear BSS problem with additive noise. The wavelet de-noising approach was first employed to eliminate the influence of the additive noise in the BSS procedure. Then, the radial basis function (RBF) neural network combined with the linear ICA was applied to the de-noised vibration signals. Vibration sources involved with the machine faults were separated. Subsequently, wavelet package decomposition (WPD) was used to extract distinct fault features from the source signals. Lastly, an RBF classifier was used to recognize the fault patterns. Field data acquired from a mining machine was used to evaluate and validate the proposed diagnostic method. The experimental analysis results show that critical fault vibration source component can be separated by the proposed method, and the fault detection rate is superior to the linear ICA based approaches.

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Tichý, Ondřej, and Václav Smídl. "Estimation of input function from dynamic PET brain data using Bayesian blind source separation." Computer Science and Information Systems 12, no. 4 (2015): 1273–87. http://dx.doi.org/10.2298/csis141201051t.

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Selection of regions of interest in an image sequence is a typical prerequisite step for estimation of time-activity curves in dynamic positron emission tomography (PET). This procedure is done manually by a human operator and therefore suffers from subjective errors. Another such problem is to estimate the input function. It can be measured from arterial blood or it can be searched for a vascular structure on the images which is hard to be done, unreliable, and often impossible. In this study, we focus on blind source separation methods with no needs of manual interaction. Recently, we developed sparse blind source separation and deconvolution (S-BSS-vecDC) method for separation of original sources from dynamic medical data based on probability modeling and Variational Bayes approximation methodology. In this paper, we extend this method and we apply the methods on dynamic brain PET data and application and comparison of derived algorithms with those of similar assumptions are given. The S-BSS-vecDC algorithm is publicly available for download.

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BOULILA, WADII, and IMED RIADH FARAH. "MULTI-APPROACH SATELLITE IMAGES FUSION BASED ON BLIND SOURCES SEPARATION." International Journal of Image and Graphics 11, no. 01 (January 2011): 117–36. http://dx.doi.org/10.1142/s0219467811004020.

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The development of satellite image acquisition tools helped improving the extraction of information about natural scenes. In the proposed approach, we try to minimize imperfections accompanying the image interpretation process and to maximize useful information extracted from these images through the use of blind source separation (BSS) and fusion methods. In order to extract maximum information from multi-sensor images, we propose to use three algorithms of BSS that are FAST- ICA2D, JADE2D, and SOBI2D. Then by employing various fusion methods such as the probability, possibility, and evidence methods we can minimize both imprecision and uncertainty. In this paper, we propose a hybrid approach based on five main steps. The first step is to apply the three BSS algorithms to the satellites images; it results in obtaining a set of image sources representing each a facet of the land cover. A second step is to choose the image having the maximum of kurtosis and negentropy. After the BSS evaluation, we proceed to the training step using neural networks. The goal of this step is to provide learning regions which are useful for the fusion step. The next step consists in choosing the best adapted fusion method for the selected source images through a case-based reasoning (CBR) module. If the CBR module does not contain a case similar to the one we are seeking, we proceed to apply the three fusion methods. The evaluation of fusion methods is a necessary step for the learning process of our CBR.

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Wang, Ping, Ji Xiang Lu, Xin Wang, and Hong Zhong Shi. "A New Orthogonal Projected Natural Gradient BSS Algorithm with a Dynamically Changing Source Number under Over-Determined Mode." Advanced Materials Research 267 (June 2011): 768–73. http://dx.doi.org/10.4028/www.scientific.net/amr.267.768.

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Blind source separation (BSS) attempts to recover unknown independent sources from a given set of observed mixtures. Algorithm based on natural gradient is one of the main methods in BSS. An analysis has been done on the problem that the old algorithm goes to diverging under over-determined mode. A new improved algorithm based on orthogonal projected natural gradient is studied in the paper. The simulated result using crosstalk error proves the capability to perform the BSS under over-determined mode and the better convergence stability of the new algorithm. It is also effective with a dynamically changing source number.

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Zhang, Junying, Le Wei, Xuerong Feng, Zhen Ma, and Yue Wang. "Pattern Expression Nonnegative Matrix Factorization: Algorithm and Applications to Blind Source Separation." Computational Intelligence and Neuroscience 2008 (2008): 1–10. http://dx.doi.org/10.1155/2008/168769.

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Independent component analysis (ICA) is a widely applicable and effective approach in blind source separation (BSS), with limitations that sources are statistically independent. However, more common situation is blind source separation for nonnegative linear model (NNLM) where the observations are nonnegative linear combinations of nonnegative sources, and the sources may be statistically dependent. We propose a pattern expression nonnegative matrix factorization (PE-NMF) approach from the view point of using basis vectors most effectively to express patterns. Two regularization or penalty terms are introduced to be added to the original loss function of a standard nonnegative matrix factorization (NMF) for effective expression of patterns with basis vectors in the PE-NMF. Learning algorithm is presented, and the convergence of the algorithm is proved theoretically. Three illustrative examples on blind source separation including heterogeneity correction for gene microarray data indicate that the sources can be successfully recovered with the proposed PE-NMF when the two parameters can be suitably chosen from prior knowledge of the problem.

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Guo, Ruiming, Zhongqiang Luo, and Mingchun Li. "A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation." Sensors 23, no. 1 (January 2, 2023): 493. http://dx.doi.org/10.3390/s23010493.

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With the advent of the era of big data information, artificial intelligence (AI) methods have become extremely promising and attractive. It has become extremely important to extract useful signals by decomposing various mixed signals through blind source separation (BSS). BSS has been proven to have prominent applications in multichannel audio processing. For multichannel speech signals, independent component analysis (ICA) requires a certain statistical independence of source signals and other conditions to allow blind separation. independent vector analysis (IVA) is an extension of ICA for the simultaneous separation of multiple parallel mixed signals. IVA solves the problem of arrangement ambiguity caused by independent component analysis by exploiting the dependencies between source signal components and plays a crucial role in dealing with the problem of convolutional blind signal separation. So far, many researchers have made great contributions to the improvement of this algorithm by adopting different methods to optimize the update rules of the algorithm, accelerate the convergence speed of the algorithm, enhance the separation performance of the algorithm, and adapt to different application scenarios. This meaningful and attractive research work prompted us to conduct a comprehensive survey of this field. This paper briefly reviews the basic principles of the BSS problem, ICA, and IVA and focuses on the existing IVA-based optimization update rule techniques. Additionally, the experimental results show that the AuxIVA-IPA method has the best performance in the deterministic environment, followed by AuxIVA-IP2, and the OverIVA-IP2 has the best performance in the overdetermined environment. The performance of the IVA-NG method is not very optimistic in all environments.

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Du, Jun, Ju Liu, Shengju Sang, and Jun Wang. "Blind separation using second order statistics for non-stationary signals." Computer Science and Information Systems 7, no. 1 (2010): 163–76. http://dx.doi.org/10.2298/csis1001163d.

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In the signal processing area, blind source separation (BSS) is a method aiming to recover independent sources from their linear instantaneous mixtures without resorting to any prior knowledge, such as mixing matrices and sources. There have been increased attentions given to blind source separation in many areas, including wireless communication, biomedical imaging processing, multi-microphone array processing, and so on in recent years. In this paper, we propose a new simple BSS technique that exploits second order statistics for nonstationary sources. Our technique utilizes the algebraic structure of the signal model and the subspace structures so as to efficiently recover sources with interference of noise. Computer simulations have demonstrated that, in comparison with other existent methods, our method has better performance in the regimes of low and medium SNRs. For high SNRs, our method is not as promising methods such as the method called AC ('alternating columns')-DC ('diagonal centers') algorithm, but it gives reasonable performance. This article has been retracted. Link to the retraction <a href="http://dx.doi.org/10.2298/CSIS1004005U">10.2298/CSIS1004005U</a>

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Journal articles on the topic 'Blind Source Separation (BSS)'

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