Blind Source Separation Method Research Articles

A low-complexity permutation alignment method for frequency-domain blind source separation

Frequency-domain blind source separation is an effective way to separate the signals from convolutive mixtures. The independence component analysis (ICA) is commonly employed to separate signals in each frequency bin, resulting in the well-known permutation problem. To resolve this problem, we present a low-complexity permutation alignment method based on the inter-frequency dependence of signal power ratio. A bin-wise permutation alignment is first carried out across all the frequency bins by measuring the correlation between the current frequency bin and the previous one, but only the permutation with a high confidence is fixed. The permutation with low confidence is then determined by maximizing the correlation between the current frequency bin and a local centroid, which is calculated from a set of determined frequency bins with high confidence. By so doing, the permutation for most frequency bins is aligned without iterations. Finally, a clustering algorithm with centroids is adopted to achieve the fine global optimization in the fullband with only a few iterations. Experiment results show that the proposed method achieves a comparable performance with the state-of-the-art permutation alignment schemes, but the new method achieves a significant computational saving.

Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning

Tool wear in machining could result in poor surface finish, excessive vibration and energy consumption. Monitoring tool wear in real-time is crucial to improve manufacturing productivity and quality. While numerous sensor-based tool wear monitoring techniques have been demonstrated in laboratory environments, few tool wear monitoring systems have been deployed in factories because it is not realistic to install some of the important sensors such as dynamometers on manufacturing machines. To address this issue, a novel audio signal processing approach is introduced. This technique does not require expensive sensors but audio sensors only. A blind source separation method is used to separate source signals from noise. An extended principal component analysis is used for dimensionality reduction. Real-time multi-channel audio signals are collected during a set of milling tests under varying cutting conditions. The experimental data are used to develop and validate a predictive model. Experimental results have shown that the predictive model is capable of classifying tool wear conditions with high accuracy.

Challenge for Affective Brain-Computer Interfaces: Non-stationary Spatio-spectral EEG Oscillations of Emotional Responses.

Electroencephalogram (EEG)-based affective brain-computer interfaces (aBCIs) have been attracting ever-growing interest and research resources. Whereas most previous neuroscience studies have focused on single-day/-session recording and sensor-level analysis, less effort has been invested in assessing the fundamental nature of non-stationary EEG oscillations underlying emotional responses across days and individuals. This work thus aimed to use a data-driven blind source separation method, i.e., independent component analysis (ICA), to derive emotion-relevant spatio-spectral EEG source oscillations and assess the extent of non-stationarity. To this end, this work conducted an 8-day music-listening experiment (i.e., roughly interspaced over 2 months) and recorded whole-scalp 30-ch EEG data from 10 subjects. Given the large size of the data (i.e., from 80 sessions), results indicated that EEG non-stationarity was clearly revealed in the numbers and locations of brain sources of interest as well as their spectral modulation to the emotional responses. Less than half of subjects (two to four) showed the same relatively day-stationary (source reproducibility >6 days) spatio-spectral tendency towards one of the binary valence and arousal states. This work substantially advances the previous work by exploiting intra- and inter-individual EEG variability in an ecological multiday scenario. Such EEG non-stationarity may inevitably present a great challenge for the development of an accurate, robust, and generalized emotion-classification model.

A computationally efficient blind source separation for hearing aid applications and its real-time implementation on smartphone

Conventional Blind Source Separation (BSS) techniques are computationally complex. This is due to the calculation of the demixing matrix for the entire signal or due to the frequent update of the demixing matrix at every time frame index, making them impractical to use in many real-time applications. In this paper, a robust, neural network based two-microphone sound source localization method is used as a criterion to enhance the efficiency of the Independent Vector Analysis (IVA), a BSS method. IVA is used to separate speech and noise sources which are convolutedly mixed. The practical usability of the proposed method is proved by implementing it on a smartphone to separate speech and noise in real-world scenarios for hearing-aid applications. The experimental results with objective and subjective tests reveal the usefulness of the developed method for real-world applications.

Speech quality enhancement using a two channel sparse adaptive filtering approach

A forward two channel blind source separation scheme can be used as a effective method for speech enhancement. In such a scheme, two mixed sound signals are used as inputs to estimate the original signals which created these mixtures. In an endeavour to enhance the speech quality, an attempt has been made in this paper to design a two channel blind source separation method based on a sparse learning strategy. The proposed approach takes advantage of the sparse nature of the acoustic path impulse responses of the mixing model and offers improved speech quality in comparison with traditional approach. Simulation study shows the improvement in speech quality in terms of perceptual evaluation of speech quality by the proposed sparse forward two channel blind source separation scheme.

Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method.

For high-speed trains, out-of-roundness (OOR)/defects on wheel tread with small radius deviation may suffice to give rise to severe damage on both vehicle components and track structure when they run at high speeds. It is thus highly desirable to detect the defects in a timely manner and then conduct wheel re-profiling for the defective wheels. This paper presents a wayside fiber Bragg grating (FBG)-based wheel condition monitoring system which can detect wheel tread defects online during train passage. A defect identification algorithm is developed to identify potential wheel defects with the monitoring data of rail strain response collected by the devised system. In view that minor wheel defects can only generate anomalies with low amplitude compared with the wheel load effect, advanced signal processing methods are needed to extract the defect-sensitive feature from the monitoring data. This paper explores a Bayesian blind source separation (BSS) method to decompose the rail response signal and to obtain the component that contains defect-sensitive features. After that, the potential defects are identified by analyzing anomalies in the time history based on the Chauvenet’s criterion. To verify the proposed defect detection method, a blind test is conducted using a new train equipped with defective wheels. The results show that all the defects are identified and they concur well with offline wheel radius deviation measurement results. Minor defects with a radius deviation of only 0.06 mm are successfully detected.

A New Nonlinear Sparse Component Analysis for a Biologically Plausible Model of Neurons

It is known that brain can create a sparse representation of the environment in both sensory and mnemonic forms (Olshausen & Field, 2004). Such sparse representation can be combined in downstream areas to create rich multisensory responses to support various cognitive and motor functions. Determining the components present in neuronal responses in a given region is key to deciphering its functional role and connection with upstream areas. One approach for parsing out various sources of information in a single neuron is by using linear blind source separation (BSS) techniques. However, applying linear techniques to neuronal spiking activity is likely to be suboptimal due to inherent and unknown nonlinearity of neuronal responses to inputs. This letter proposes a nonlinear sparse component analysis (SCA) method to separate jointly sparse inputs to neurons with post summation nonlinearity, or SCA for post-nonlinear neurons (SCAPL). Specifically, a linear clustering approach followed by principal curve regression (PCR) and a nonlinear curve fitting are used to separate sources. Analysis using simulated data shows that SCAPL accuracy outperforms ones obtained by linear SCA, as well as other separating methods, including linear independent and principal component analyses. In SCAPL, the number of derived sparse components is not limited by the number of neurons, unlike most BSS methods. Furthermore, this method allows for a broad range of post-summation nonlinearities that could differ among neurons. The sensitivity of our method to noise, joint sparseness, degree, and shape of nonlinearity and mixing ill conditions is discussed and compared to existing methods. Our results show that the proposed method can successfully separate input components in a population of neurons provided that they are temporally sparse to some degree. Application of SCAPL should facilitate comparison of functional roles across regions by parsing various elements present in a region.

Implementation and evaluation distributed mixed pixels analysis algorithm for hyperspectral image based on constraint non-negative matrix factorization

As an effective blind source separation method, non-negative matrix factorization has been widely adopted to analyze mixed data in hyperspectral images. To avoid trapping in local optimum, appropriate constraints are added to the objective function of NMF, whose reflections of image essential attribute determine the performance finally. In this paper, a new NMF-based mixed data analysis algorithm is presented, with maximum overall coverage constraint introduced in traditional NMF. The new constraint was proposed using data geometrical properties in the feature space to maximizes the number of pixels contained in the simplex constructed by endmembers compulsorily and introduced in objective function of NMF, named maximum overall coverage constraint NMF (MOCC-NMF), to analyze mixed data in highly mixed hyperspectral data without pure pixels. For implementing easily, multiplicative update rules are applied to avoid step size selection problem occurred in traditional gradient-based optimization algorithm frequently. Furthermore, in order to handle huge computation involved, parallelism implementation of the proposed algorithm using MapReduce is described and the new partitioning strategy to obtain matrix multiplication and determinant value is discussed in detail. In the numerical experiments conducted on real hyperspectral and synthetic datasets of different sizes, the efficiency and scalability of the proposed algorithm are confirmed.

Novel method for component separation of extended sources in X-ray astronomy

In high-energy astronomy, spectro-imaging instruments such as X-ray detectors allow investigation of the spatial and spectral properties of extended sources including galaxy clusters, galaxies, diffuse interstellar medium, supernova remnants, and pulsar wind nebulae. In these sources, each physical component possesses a different spatial and spectral signature, but the components are entangled. Extracting the intrinsic spatial and spectral information of the individual components from this data is a challenging task. Current analysis methods do not fully exploit the 2D-1D (x, y, E) nature of the data, as spatial information is considered separately from spectral information. Here we investigate the application of a blind source separation (BSS) algorithm that jointly exploits the spectral and spatial signatures of each component in order to disentangle them. We explore the capabilities of a new BSS method (the general morphological component analysis; GMCA), initially developed to extract an image of the cosmic microwave background from Planck data, in an X-ray context. The performance of the GMCA on X-ray data is tested using Monte-Carlo simulations of supernova remnant toy models designed to represent typical science cases. We find that the GMCA is able to separate highly entangled components in X-ray data even in high-contrast scenarios, and can extract the spectrum and map of each physical component with high accuracy. A modification of the algorithm is proposed in order to improve the spectral fidelity in the case of strongly overlapping spatial components, and we investigate a resampling method to derive realistic uncertainties associated to the results of the algorithm. Applying the modified algorithm to the deep Chandra observations of Cassiopeia A, we are able to produce detailed maps of the synchrotron emission at low energies (0.6–2.2 keV), and of the red- and blueshifted distributions of a number of elements including Si and Fe K.

Changes in motor unit discharge patterns following strength training.

Changes in motor unit discharge patterns following strength training.

A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy

In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world scenarios, artifact rejection is essential. However, currently there exists no gold-standard. Although a plenitude of methodological approaches implicitly assume the presence of latent processes in the signals, elaborate Blind-Source-Separation methods have rarely been applied. A reason are challenging characteristics such as Non-instantaneous and non-constant coupling, correlated noise and statistical dependencies between signal components. We present a novel suitable BSS framework that tackles these issues by incorporating A) Independent Component Analysis methods that exploit both higher order statistics and sample dependency, B) multimodality, i.e., fNIRS with accelerometer signals, and C) Canonical-Correlation Analysis with temporal embedding. This enables analysis of signal components and rejection of motion-induced physiological hemodynamic artifacts that would otherwise be hard to identify. We implement a method for Blind Source Separation and Accelerometer based Artifact Rejection and Detection (BLISSA2RD). It allows the analysis of a novel n-back based cognitive workload paradigm in freely moving subjects, that is also presented in this manuscript. We evaluate on the corresponding data set and simulated ground truth data, making use of metrics based on 1st and 2nd order statistics and SNR and compare with three established methods: PCA, Spline and Wavelet-based artifact removal. Across 17 subjects, the method is shown to reduce movement induced artifacts by up to two orders of magnitude, improves the SNR of continuous hemodynamic signals in single channels by up to 10dB, and significantly outperforms conventional methods in the extraction of simulated Hemodynamic Response Functions from strongly contaminated data. The framework and methods presented can serve as an introduction to a new type of multivariate methods for the analysis of fNIRS signals and as a blueprint for artifact rejection in complex environments beyond the applied paradigm.

Post-nonlinear blind source separation with kurtosis constraints using augmented Lagrangian particle swarm optimization and its application to mechanical systems

To accurately estimate source signals from their post-nonlinear mixtures, a post-nonlinear blind source separation (PNLBSS) method with kurtosis constraints is proposed based on augmented Lagrangian particle swarm optimization (PSO). First, an improved contrast function is presented by combining mutual information of the separated signals and kurtosis ranges of source signals. Second, an augmented Lagrangian multiplier method is used to convert PNLBSS into an unconstrained pseudo-objective optimization problem. Then, improved PSO is applied to update the parameters in complex nonlinear spaces. Finally, numerical case studies and experimental case studies are provided to evaluate the performance of the proposed method. By adding the kurtosis ranges constraints, the estimation accuracy of source signals could be improved, which would benefit vibration and acoustic monitoring and control.

Suppression of Impulsive Noise in Power Line Communication using Weighted FastICA

In this paper, a weighted blind source separation (BSS) method is developed to eliminate α -stable distribution impulsive noise in the power line communication (PLC). In this work, a weighted independent component analysis(ICA) is conceived to construct determined ICA model from a single channel observation. This operation can achieve the advantages of low complexity blind separation work and exempting channel parameter estimation. The system noise model is described by the transmission characteristic of power line. Simulation experiments show that the proposed ICA algorithm can effectively achieve noise elimination, and guarantee the practical communication requirements.

Improving Neutron-Gamma Discrimination with Stilbene Organic Scintillation Detector Using Blind Nonnegative Matrix and Tensor Factorization Methods

In order to perform highly qualified neutron-gamma discrimination in mixed radiation field, we investigate the application of blind source separation methods based on nonnegative matrix and tensor factorization algorithms as new and robust neutron-gamma discrimination software-based approaches. These signal processing tools have allowed to recover original source components from real-world mixture signals which have been recorded at the output of the stilbene scintillation detector. The computation of the performance index of separability of each tested nonnegative algorithm has allowed to select Second-Order NMF algorithm and NTF-2 model as the most efficient techniques for discriminating neutrons and gammas. Furthermore, the neutron-gamma discrimination is highlighted through the computation of the cross-correlation function. The performance of the blind source separation methods has been quantified through the obtained results that prove a good neutron-gamma separation.

A novel underdetermined blind source separation method with noise and unknown source number

It has been challenging to correctly separate sources from their mixtures with large noise and unknown source number in underdetermined cases. To address this problem, a novel underdetermined blind source separation (UBSS) method is proposed using synchrosqueezing transform (SST) and improved density peaks clustering (DPC). First, SST is employed to reassign time-frequency (TF) coefficients of short time Fourier transform (STFT) of mixed signals from their original position to the center of gravity along frequency direction, contributing to sparser TF representation than STFT. Second, a single source point (SSP) identification method is proposed by directly searching the identical normalized TF vectors, which considers linear representation relations among TF vectors and therefore can identify SSPs more accurately in noisy cases. Third, DPC is improved to automatically identify source number, with which the mixing matrix can be estimated. After that, sources are finally recovered using l1-norm decomposition method. The effectiveness of the proposed method is validated with some numerical studies and experimental studies. The results show that the proposed method can estimate source number correctly, and recover sources accurately even in noisy cases.

An Unsupervised Method for Artefact Removal in EEG Signals.

Objective: The activity of the brain can be recorded by means of an electroencephalogram (EEG). An EEG is a multichannel signal related to brain activity. However, EEG presents a wide variety of undesired artefacts. Removal of these artefacts is often done using blind source separation methods (BSS) and mainly those based on Independent Component Analysis (ICA). ICA-based methods are well-accepted in the literature for filtering artefacts and have proved to be satisfactory in most scenarios of interest. Our goal is to develop a generic and unsupervised ICA-based algorithm for EEG artefacts removal. Approach: The proposed algorithm makes use of a new unsupervised artefact detection, ICA and a statistical criterion to automatically select the artefact related independent components (ICs) requiring no human intervention. The algorithm is evaluated using both simulated and real EEG data with artefacts (SEEG and AEEG). A comparison between the proposed unsupervised selection of ICs related to the artefact and other supervised selection is also presented. Main results: A new unsupervised ICA-based algorithm to filter artefacts, where ICs related to each artefact are automatically selected. It can be used in online applications, it preserves most of the original information among the artefacts and removes different types of artefacts. Significance: ICA-based methods for filtering artefacts prevail in the literature. The work in this article is important insofar as it addresses the problem of automatic selection of ICs in ICA-based methods. The selection is unsupervised, avoiding the manual ICs selection or a learning process involved in other methods. Our method is a generic algorithm that allows removing EEG artefacts of various types and, unlike some ICA-based algorithms, it retains most of the original information among the artefacts. Within the algorithm, the artefact detection method implemented does not require human intervention either.

Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis

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.

Designing Efficient Blind Source Separation Methods for EEG Motion Artifact Removal Based on Statistical Evaluation

As the electroencephalography (EEG) biomedical signals are affected under the presence of the muscular motion artifacts. Presence of these artifacts leads to error in visual analysis of EEG signal, thus results in wrong diagnosis of human diseases. The variants of blind source separation (BSS) methods are available. This paper aims to design the efficient BSS based method for effectively eradicating the EEG motion artifacts. This is accomplished by evaluating the six different methods, which are combination of independent component analysis (ICA) and canonical correlation analysis (CCA) along with the discrete wavelet transform and stationary wavelet transform methods. Each of above combination methods are applied on the ensemble empirical mode decomposed, Intrinsic Mode Functions for EEG motion artifact suppression. This research paper tests the performance over pure EEG signal and also on the simulated EEG sinusoids to mimic the effect of motion artifacts. The performance of six BSS artifact removal algorithms are evaluated using efficiency matrices such as del signal to noise ratio, lambda (λ), spectral distortion (Pdis) and root mean square error. The execution time is also calculated to evaluate the computation efficiency of the algorithms. The results suggest that CCA algorithm outperforms over ICA in the case of the high noisy condition of EEG signal.

Removing Muscle Artifacts From EEG Data via Underdetermined Joint Blind Source Separation: A Simulation Study

Electroencephalography (EEG) recordings are often contaminated by artifacts from electromyogram (EMG). This artifact not only affects the visual analysis but also strongly impedes its various usages in biomedical research. With a sufficient number of EEG recordings, numerous blind source separation (BSS) methods can be applied to suppress or remove such EMG artifacts. However, in many practical applications (e.g., ambulatory health-care monitoring), the number of EEG sensors is often limited, while conventional BSS methods (e.g., independent component analysis) may fail to work in such cases. Considering the increasing need for acquiring EEG signals in ambulatory environments, we propose a novel underdetermined joint BSS method to remove EMG artifacts from EEG data with a limited number of EEG sensors. The performance of the proposed method is evaluated through numerical simulations in which EEG recordings are contaminated with muscle artifacts. The results demonstrate that the proposed method can effectively remove muscle artifacts while preserving EEG signals successfully.

A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation.

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.