Underdetermined blind source separation algorithm of 220kV substation noise based on SCA

The problem of underdetermined blind source separation is addressed. An advanced classification method based upon competitive learning is proposed for automatically determining the number of active sources over the observation. Its introduction in underdetermined blind source separation successfully overcomes the drawback of an existing method, in which the goal of separating more sources than the number of available mixtures is achieved by exploiting the sparsity of the nonstationary sources in the time-frequency domain. Simulation studies are presented to support the proposed approach.

To address the significant performance degradation of conventional underdetermined blind source separation algorithms for frequency-hopping (FH) signals under time-frequency (TF) overlapping conditions, this brief presents a novel three-stage scheme based on the TF distribution of FH signals. In the first stage, key parameters of the FH signal are estimated using a TF binary graph. In the second step, the initial mixing matrix is estimated for non-overlapping and overlapping carrier frequencies employing density peaks clustering and tensor decomposition methods, respectively. In the third step, the final mixing matrix, directions of arrival (DOA), and source signals are estimated using the expectation-maximization algorithm within the nonnegative matrix factorization model. Finally, different segments of the FH signals are spliced together based on the DOAs of different source signals. Comprehensive experimental results demonstrate the superior performance of the proposed algorithm compared to state-of-the-art algorithms. Even at a signal-to-noise ratio of 5 dB, the correlation coefficient of the estimated source signals can still reach 0.91.

We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (1) frequency-domain ICA with direction-of-arrival (DOA) estimation; (2) null beamforming based on the estimated DOA; (3) integration of (1) and (2) based on the algorithm diversity in both iteration and frequency domain. The inverse of the mixing matrix obtained by ICA is temporally substituted by the matrix based on null beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.

A new blind image source separation algorithm based on feedback sparse component analysis

With the increasing investment in the construction of power facilities and the use of higher voltage level, the noise generated by high voltage substation has become an unavoidable issue. The noise of the high-voltage substation is a mixture which is mixed by different noise sources. The mixed noise must be separated to several independent noise signals firstly for researching. An algorithm combined with the Wavelet Transform and the Sparse Component Analysis was presented in this paper which can separate the single channel mixed noise signal of high-voltage substation. The single channel blind source separation can be changed to the underdetermined blind source separation by setting two augmented signals which are the low-frequency part and the high-frequency part separated from the original noise signal by the Wavelet Transform. Then the signals of different noise sources can be separated by the Sparse Component Analysis. It is proved by the simulation and experiment in 330kV high-voltage substation that the algorithm can solve not only the underdetermined blind source separation, but also the single-channel blind source separation effectively.

Blind single-channel source separation is a long-standing machine learning and signal processing problem. Traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints. Generative adversarial network (GAN) are free from statistical constraints and samples, but the role of adversarial training in the task of BSS has not been fully demonstrated. Therefore, a new separation network model that enables to learn the known separated signal distribution from the stepwise fine estimation of the unknown mixture distribution was presented in this paper, and a self-attention mechanism was introduced to solve the problem of the blurring details of the generated image by the generator which preserves image details in the process of image separation. Compared with the existing single-channel blind source separation algorithm based on generative adversarial network-neural egg separation (NES), the detailed information of this new separation algorithm is more prominent, and the source signal in the mixed image has been separated more effectively, and has better separation performance than the classic blind source separation algorithms.

Underdetermined blind source separation (UBSS) deals with the problem of estimating n source signals from m measurements (n > m), with an unknown mixing process. Most researches pay attention to the sparsity of speech to recover source signals, such as the DUET (degenerate unmixing estimation technique) algorithm, which can separate any number of sources using only two mixtures with the help of estimation of mixing parameters but cant be used in the time-varying system. We develop a block-online algorithm for a dynamic environment that is based on mixing parameter estimation from reliable time-frequency points determined by the total power and FCM (Fuzzy C-Means) clustering of parameter estimates for every time-frame. To track the variable mixing parameters, the estimated ones from the previous frame can be used to initialize FCM in the current frame with changed mixing measure instead of random initialization in case of the conventional FCM. Thus, an online adaptive algorithm of UBSS with the time-varying matrix is realized.

We propose a new algorithm for blind source separation (BSS), in which independent component analysis (ICA) and beamforming are combined to resolve the low-convergence problem through optimization in ICA. The proposed method consists of the following three parts: (1) frequency-domain ICA with direction-of-arrival (DOA) estimation, (2) null beamforming based on the estimated DOA, and (3) integration of (1) and (2) based on the algorithm diversity in both iteration and frequency domain. The inverse of the mixing matrix obtained by ICA is temporally substituted by the matrix based on null beamforming through iterative optimization, and the temporal alternation between ICA and beamforming can realize fast- and high-convergence optimization. The results of the signal separation experiments reveal that the signal separation performance of the proposed algorithm is superior to that of the conventional ICA-based BSS method, even under reverberant conditions.

The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-means clustering to yield improvements in separation ability for underdetermined situations using a nonlinear microphone array. However, rather than test the blind source separation ability solely on reverberant conditions, this paper extends this to include a variety of simulated and real-world noisy environments. Results reported encouraging separation ability and improved perceptual quality of the separated sources for such adverse conditions. Not only does this establish this proposed methodology as a credible improvement to the system, but also implies further applicability in areas such as noise suppression in adverse acoustic environments.

One (of) important application of sparse component analysis (SCA) is in underdetermined blind source separation (BSS). Within a probability framework, this paper focuses on recoverability problem of underdetermined BSS based on a two-stage SCA approach. We consider a general case in which both sources and mixing matrix are randomly drawn. First, we present a recoverability probability estimate under the condition that the nonzero entry number of a source column vector is fixed. Next, we define the sparsity degree of a signal, and establish the relationship between the sparsity degree of sources and recoverability probability. Finally, we explain how to use the relationship to guarantee the performance of BSS. Several simulation results have demonstrated the validity of the probability estimation approach.

This work presents the application of Blind Source Separation (BSS) Algorithms on Dual-band IR Spectrogram for breast cancer detection and tracing the effect of long-term chemotherapy for breast-cancer patients. We take Dual-band IR Spectrogram's RAW Data as an input to the BSS algorithms. Integrated into the back-end processor of a Dual-band IR Sensor and Readout Circuit Platform, our Improved Neighbor-based BSS algorithm operated on Dual-band IR Spectrogram provides a better breast cancer detection. Considering the demarcating degree, our Improved Neighbor-based BSS algorithm is approximately 15% better than other algorithms. As to the correctness rate, our improved algorithm approximately increases 10% compared with other algorithms.

This paper describes a novel modification to the well-known natural gradient or INFOMAX algorithm for blind source separation that largely mitigates its divergence problems. The modified algorithm imposes an a posteriori scalar gradient constraint that adds little computational complexity to the algorithm and exhibits fast convergence and excellent performance for fixed step size values that are largely independent of input signal magnitudes and initial separation matrix estimates. Evaluation of the approach for the separation of instantaneous and convolutive source mixtures using both time- and frequency-domain implementations shows its excellent separation behavior.

Most algorithms for blind source separation (BSS) or independent component analysis (ICA) assume an equal number of sources as sensors. For multichannel electrophysiological recordings, such as the electroencephalogram (EEG), however, there are often far fewer sources of neurophysiologically relevant activity than the number of sensors. This adds a model order estimation problem to the source separation problem. Conventional estimates of the number of sources are based on the dominant eigenvalues of the data covariance matrix, obtained from principal component analysis (PCA), whose corresponding eigenvectors are also used for prewhitening. It is well known that PCA is susceptible to noise, leading to incorrect model order estimates and data distortion, which in turn limit the accuracy of the source estimates. It is therefore highly desirable to determine the correct number of sources and their spatial topographies directly, without PCA-based data truncation or prewhitening. In this work, we present a stepwise BSS method for extracting only the sources necessary for a sufficiently good least-square fit to the data. This simultaneously yields model order and source estimates, which we examine at different noise levels. We also show how only a few neurophysiologically meaningful components can be extracted from 25-channel ictal EEG.

For linear mixing model (LMM) of hyperspectral unmixing in hyperspectral images processing problem, the endmember fractional abundances satisfy the sum-to-one constraint, which makes the well-known independent component analysis (ICA) based blind source separation (BSS) algorithms not well suited to blind hyperspectral unmixing (bHU). In this paper, an efficient non-parametric bHU algorithm consulting dependent component analysis (DCA) is presented. Based on the cumulative density function (CDF) and order statistics instead of traditional probability distribution function (PDF), the novel objective function is derived by maximising the non-parametric non-Gaussianity between the estimated endmember abundance of the endmember signatures and their corresponding original abundances. With the stochastic gradient rule of constrained optimisation method, an efficient dependent sources separation algorithm for bHU is obtained to fulfil the endmember signatures extraction and abundances estimation tasks. Simulations based on the synthetic data are performed to evaluate the validity of the proposed non-parametric non-Gaussianity HU (non-pNG-bHU) algorithm.

Blind source separation (BSS) algorithms are often categorized as either narrowband or broadband algorithms depending on whether their respective cost functions aim at individual DFT bins or the entire broadband signal. In this contribution, we present comparable general natural gradient-based formulations of both concepts based on the TRINICON framework. As a distinctive feature, narrowband algorithms imply an internal permutation and scaling problem. We show that the common DOA estimation-based methods for aligning the permutations effectively rely on geometric a-priori knowledge, and we explain why they need to be complemented by additional repair mechanisms for robust BSS. The latter can already be viewed as approximations of the generic TRINICON broadband algorithm. As a conclusion, we propose to always use a generic broadband algorithm as a starting point for the design of new BSS algorithms.