Source Separation Method Research Articles

Utilizing data-driven algorithms for blind modal parameter identification of structures from output-only video measurements

In structural dynamics and vibration analysis, modal identification plays a pivotal role in understanding and characterizing the dynamic behavior of complex systems. Traditional modal analysis techniques heavily rely on accurate sensor placement and knowledge of the excitation forces, which may not always be feasible or practical in real-world scenarios. Recently, non-contact video-based modal analysis methods for structures with arbitrary complexity using computer vision techniques have gained much importance. But these methods need to know ahead of time where the structure's natural frequency ranges are, and they use steerable pyramids, which are complicated multi-scale image decomposition filters. To address these issues, a blind modal identification technique is suggested to extract the modal parameters (modal frequency and mode shapes) from the recorded structural vibration video signal. The proposed algorithm for unsupervised machine learning is the Non-Negative Matrix Factorization (NNMF) method, which is combined with the Generalized Complexity Pursuit (GCP) method for blind source separation. The NNMF algorithm is directly applied to the raw pixel-time series made from the video data to get the temporal components, which are then separated using GCP to find the individual modal frequencies and mode shapes. The above algorithm is first validated on a numerical model and then implemented on laboratory scale models (cantilever, single-degree, and multi-degree frame models). Finally, the proposed methodology is implemented on real-world recorded structural vibration videos to determine its noise sensitivity, such as the Tacoma Narrows bridge and the London Millennium footbridge. The modal parameters extracted are compared with those from the available literature for validation. The estimation errors obtained from all the validations are well below 1%, which makes the technique quite suitable and reliable for structural vibration monitoring by identifying and reproducing complex vibration modes.

Generalized channel separation algorithms for accurate camera-based multi-wavelength PTT and BP estimation

Single-site multi-wavelength (MW) pulse transit time (PTT) measurement was recently proposed using contact sensors with sequential illumination. It leverages different penetration depths of light to measure the traversal of a cardiac pulse between skin layers. This enabled continuous single-site MW blood pressure (BP) monitoring, but faces challenges like subtle skin compression, which importantly influences the PPG morphology and subsequent PTT. We extended this idea to contact-free camera-based sensing and identified the major challenge of color channel overlap, which causes the signals obtained from a consumer RGB camera to be a mixture of responses in different wavelengths, thus not allowing for meaningful PTT measurement. To address this, we propose novel camera-independent data-driven channel separation algorithms based on constrained genetic algorithms. We systematically validated the algorithms on camera recordings of palms and corresponding ground-truth BP measurements of 13 subjects in two different scenarios, rest and activity. We compared the proposed algorithms against established blind source separation methods and against previous camera-specific physics-based method, showing good performance in both PTT reconstruction and BP estimation using a Random Forest regressor. The best-performing algorithm achieved mean absolute errors (MAEs) of 3.48 and 2.61 mmHg for systolic and diastolic BP in a leave-one-subject-out experiment with personalization, solidifying the proposed algorithms as enablers of novel contact-free MW PTT and BP estimation.

Real-time, sample-by-sample estimation of multiple signal waveforms from acoustic array data using B-splines

This work presents a real-time signal processing algorithm that estimates time-domain waveforms of multiple plane wave signals on a sample-by-sample basis from data measured by an acoustic array. Moreover, the algorithm provides sample-by-sample estimates of the direction-of-arrival (DOA) of the waveforms. In this case sample-by-sample means that as each sample of data is measured by the array estimates of the waveforms and their directions-of-arrival are updated. While the basic idea can be based on almost any finite-dimensional approximation to a function space, this algorithm makes use of B-spline basis for the signal waveforms. Other features of the approach include no restrictions on signal waveform shape, automatic estimation of the number of signals present, automatic step-size adjustment for correction of signal waveform and direction-of-arrival parameters to achieve good performance while guaranteeing stability, and the use of circular (spherical) statistical models for 2-D (3-D) DOA estimates. The approach contrasts with blind source separation (BSS) methods that are based on non-Gaussian statistical assumptions and that do not assume a known array geometry nor a propagation model. This method is closely related to algorithms based on block-of-data by block-of-data processing but does not require stitching results from block processing to create time-domain waveform estimates.

Relationship between intelligibility, naturalness, and listening effort of source-separated speech

Various signal processing techniques have been proposed for separating a speech signal from an acoustic environment that includes other sound sources. One important measure of success of a source separation method is the intelligibility of the extracted speech signal, determined as the fraction of intended words correctly recognized. However, even for extracted signals with similar intelligibility, listeners may experience different reactions to the result. Speech samples extracted by different techniques may differ in the degree to which they sound pleasant or natural to a listener, and/or the degree of cognitive effort required to understand them. We present results of an experiment in which listeners were presented with context-free spondees recorded in a noisy environment and subsequently processed in various ways to enhance the speech and suppress the noise. Listeners transcribed the speech and also judged the “naturalness” and “listening effort” of the speech. Intentionally, stimulus intelligibility varied widely—as did naturalness and effort. We present and discuss the measured relationship among intelligibility, perceived naturalness, and reported listening effort.

Flux Contribution and Geometry of Charge Exchange Emission in the Starburst Galaxy M82

Recent X-ray studies of starburst galaxies have found that charge exchange (CX) commonly occurs between outflowing hot plasma and cold gas, possibly from swept-up clouds. However, the total CX flux and the regions where CX occurs have been poorly understood. We present an analysis of XMM-Newton observations of M82, a prototype starburst galaxy, aiming to investigate these key properties of CX emission. We have used a blind source separation method in an image analysis of CCD data, which identified a component with the enhanced O–K lines expected from the CX process. Analyzing the XMM-Newton/RGS spectra from the regions identified by the image analysis, we have detected a high forbidden-to-resonance ratio of the O vii Heα triplet as well as several emission lines from K-shell transitions of C, N, and O that are enhanced by the CX process. CX is less responsible for the emission lines of Ne and Mg, and accurate estimation of the CX contribution is confirmed to be crucial in measuring chemical abundances. The temperature of the plasma acting as an electron receiver in the CX process is significantly lower compared to that of the plasma components responsible for most of the X-rays. From the low temperature and an estimation of the CX-emitting volume, we find that the CX primarily occurs in a limited region at interfaces between plasma and gas whose temperatures rapidly decrease due to thermal conduction.

Towards vision-based structural modal identification at low frame rate using blind source separation

With increasing availability of cost-effective and high-resolution cameras, their use as a non-contact sensing tool has rapidly progressed for structural health monitoring. The cameras offer unique capabilities to provide full-field measurement with high spatial density at low cost. However, extracting high-density temporal data is challenging, as a high-speed camera increases the monitoring cost with high-rate data processing. Recently, motion magnification (MM) has shown significant success in analyzing low-amplitude motion of structural systems. However, previous studies observed that MM methodology performs poorly at low frame rates for modal identifications. In this paper, the influence of low frame rate on phased-based motion magnification (PMM) has been investigated. A novel technique is proposed by combining PMM with zero mean-normalization cross-correlation tracker to determine vibrational responses, and then the spatial Wigner-Ville spectrum-based time-frequency blind source separation method is explored for modal identification using the extracted vibrational responses obtained from the video data. The experimental data of a lumped mass experimental model and a steel bridge is used to test the accuracy of the proposed method. The original and motion-magnified image response data is compared with accelerometer data for modal identification. The proposed method is able to extract the modal parameters with high accuracy for motion-magnified images, even for low frame rates.

Nonlinear spatio-temporal filter to reduce crosstalk in bipolar electromyogram

Objective. The wide detection volume of surface electromyogram (EMG) makes it prone to crosstalk, i.e. the signal from other muscles than the target one. Removing this perturbation from bipolar recordings is an important open problem for many applications. Approach. An innovative nonlinear spatio-temporal filter is developed to estimate the EMG generated by the target muscle by processing noisy signals from two bipolar channels, placed over the target and the crosstalk muscle, respectively. The filter is trained on some calibration data and then can be applied on new signals. Tests are provided in simulations (considering different thicknesses of the subcutaneous tissue, inter-electrode distances, locations of the EMG channels, force levels) and experiments (from pronator teres and flexor carpi radialis of 8 healthy subjects). Main results. The proposed filter allows to reduce the effect of crosstalk in all investigated conditions, with a statistically significant reduction of its root mean squared of about 20%, both in simulated and experimental data. Its performances are also superior to those of a blind source separation method applied to the same data. Significance. The proposed filter is simple to be applied and feasible in applications in which single bipolar channels are placed over the muscles of interest. It can be useful in many fields, such as in gait analysis, tests of myoelectric fatigue, rehabilitation with EMG biofeedback, clinical studies, prosthesis control.

Human arm redundancy: a new approach for the inverse kinematics problem.

The inverse kinematics (IK) problem addresses how both humans and robotic systems coordinate movement to resolve redundancy, as in the case of arm reaching where more degrees of freedom are available at the joint versus hand level. This work focuses on which coordinate frames best represent human movements, enabling the motor system to solve the IK problem in the presence of kinematic redundancies. We used a multi-dimensional sparse source separation method to derive sets of basis (or source) functions for both the task and joint spaces, with joint space represented by either absolute or anatomical joint angles. We assessed the similarities between joint and task sources in each of these joint representations, finding that the time-dependent profiles of the absolute reference frame's sources show greater similarity to corresponding sources in the task space. This result was found to be statistically significant. Our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, allowing for simple transformations between task and joint spaces. Additionally, joint space seems to be represented in an absolute reference frame to simplify the IK transformations, given redundancies. Further studies will assess this finding's generalizability and implications for neural control of movement.

Study on weak sound signal separation and pattern recognition under strong background noise in marine engineering

The extraction of weak acoustic signals under strong background noise is of great significance in the applications of target identification and localization. In this paper, the pulse signal with high randomness is set as the weak signal sound source, random noise and sine sound are used as the background noise. Under the condition of a signal-to-noise ratio of −20 dB, combined with blind source separation and neural network methods, the collected observation signals are subjected to weak sound signal separation and recognition research. The optimization method of centralization and scaling processing is used to eliminate the unfavorable influence of the uncertainty of the separated signal amplitude caused by the blind source separation method on the pattern recognition. The recognition result is verified by the combination of “weak impulse acoustic signal” and “random noise signal,” and the output vector (0.99 0.01 0.01) approaches (1 0 0), which is recognized as impulse acoustic signal. By combining blind source separation and neural network methods, the separation and identification of weak pulse signals under the condition of a signal-to-noise ratio of −20 dB can be achieved.

Clutter rejection in passive non-line-of-sight imaging via blind multispectral unmixing.

Passive non-line-of-sight imaging methods that utilize scattered light to "look around corners" are often hindered by unwanted sources that overwhelm the weaker desired signal. Recent approaches to mitigate these "clutter" sources have exploited dependencies in the spectral content, or color, of the scattered light. A particularly successful method utilized blind source separation methods to isolate the desired imaging signal with minimal prior information. This current paper quantifies the efficacy of several preconditioning and unmixing algorithms when blind source separation methods are employed for passive multispectral non-line-of-sight imaging. Using an OLED television monitor as the source of both the desired signals and clutter, we conducted multiple controlled experiments to test these methods under a variety of scene conditions. We conclude that the preconditioner is a vital component as it greatly decreases the power and correlation of the clutter. Additionally, the choice of unmixing algorithm significantly impacts the reconstruction quality. By optimizing these two components, we find that effective image retrieval can be obtained even when the clutter signals are as much as 670 times stronger than the desired image.

CW_ICA: an efficient dimensionality determination method for independent component analysis.

Independent component analysis (ICA) is a widely used blind source separation method for signal pre-processing. The determination of the number of independent components (ICs) is crucial for achieving optimal performance, as an incorrect choice can result in either under-decomposition or over-decomposition. In this study, we propose a robust method to automatically determine the optimal number of ICs, named the column-wise independent component analysis (CW_ICA). CW_ICA divides the mixed signals into two blocks and applies ICA separately to each block. A quantitative measure, derived from the rank-based correlation matrix computed from the ICs of the two blocks, is utilized to determine the optimal number of ICs. The proposed method is validated and compared with the existing determination methods using simulation and scalp EEG data. The results demonstrate that CW_ICA is a reliable and robust approach for determining the optimal number of ICs. It offers computational efficiency and can be seamlessly integrated with different ICA methods.

Earth's core variability from magnetic and gravity field observations

Abstract. The motions of the liquid within the Earth's outer core lead to magnetic field variations together with mass distribution changes. As the core is not accessible for direct observation, our knowledge of the Earth’s liquid core dynamics only relies on indirect information sources. Mainly generated by the core dynamics, the surface geomagnetic field provides information about the variations of the fluid motion at the top of the core. The dynamic of the fluid core is also associated with mass distribution changes inside the core and produces gravitational field time fluctuations. By applying several statistical blind source separation methods to both the gravity and magnetic field time series, we investigate the common space–time variabilities. We report several robust interannual oscillations shared by the two observation sets. Among those, a common mode of around 7 years looks very significant. Whereas the nature of the driving mechanism of the coupled variability remains unclear, the spatial and temporal properties of the common signal are compatible with a core origin.

Robust second-order stationary spatial blind source separation using generalized sign matrices

Consider a spatial blind source separation model in which the observed multivariate spatial data are assumed to be a linear mixture of latent stationary spatially uncorrelated random fields. The objective is to recover an unknown mixing procedure as well as the latent random fields. Recently, spatial blind source separation methods that are based on the simultaneous diagonalization of two or more scatter matrices were proposed. In cases involving uncontaminated data, such methods can solve the blind source separation problem, however, in the presence of outlying observations, these methods perform poorly. We propose a robust blind source separation method that employs robust global and local covariance matrices based on generalized spatial signs in simultaneous diagonalization. Simulation studies are employed to illustrate the robustness and efficiency of the proposed methods in various scenarios.

A novel semi-blind source separation framework towards maximum signal-to-interference ratio

For the independence-based blind source separation (BSS) methods, effective modeling of the source signals is important. When applying the auxiliary function techniques, different source models normally result in different forms of the weighted covariance matrices, which in turn are used to compute the demixing filters. This paper proposes a new algorithmic framework termed minimum variance independent component analysis (MVICA), which is rigorously derived from the conventional BSS methods by determining the weighted covariance matrices with the maximum signal-to-interference ratio (SIR) criterion.The maximum-SIR weighted covariance matrix is approximately proved to be the interference covariance matrix. A deep neural network-supported implementation of the proposed MVICA algorithm is subsequently presented. Furthermore, we give a revealing insight into the relationships between MVICA and the existing speech separation approaches including conventional BSS methods and several mainstream beamforming techniques. Experimental results show the superiority of MVICA over the state-of-the-art BSS techniques and beamformers under various conditions, in terms of not only SIR but also signal-to-distortion ratio, speech intelligibility and perceptual quality and automatic speech recognition accuracy.

A comparative study of blind source separation methods

A comparative study of blind source separation methods

A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components

The conventional dictionary learning (DL) algorithms aim to adapt the dictionary/sparse code to individual functional magnetic resonance imaging (fMRI) data. Thus, lacking the capability to consolidate the spatiotemporal diversities offered by other subjects. Considering that subject-wise (sw) data matrix can be decomposed into the sparse linear combination of multi-subject (MS) time courses and MS spatial maps, two new algorithms, sw sequential DL (swsDL) and sw block DL (swbDL), have been proposed. They are based on the novel framework, defined by the mixing model, where base matrices prepared by operating a computationally fast sparse spatiotemporal blind source separation method over multiple subjects are employed to adapt the mixing matrices to sw training data. They solve the optimization models formulated using l_0/l_1-norm penalization/constraints through dictionary/sparse code pair update and alternating minimization approach. They are unique because no existing sparse DL method can incorporate MS spatiotemporal components while updating sw atoms/sparse codes, which can eventually be assembled using neuroscience knowledge to extract group-level dynamics. Various fMRI datasets are used to evaluate and compare the performance of the proposed algorithms with existing state-of-the-art algorithms. Specifically, overall, a 14% increase in the mean correlation value and 39% reduction in the mean computation time exhibited by swsDL and swbDL, respectively, over the adaptive consistent sequential dictionary algorithm.

Blind source separation of electromagnetic signals based on deep focusing U-Net

The unintentional electromagnetic radiation of digital electronic devices during operation can cause information leakage and threaten the information security of the system. In order to explore the leakage level of important information, it is necessary to separate the electromagnetic leakage signal from the complex environmental electromagnetic wave, so the blind source separation technology is studied.Traditional blind source separation methods are mainly unsupervised learning methods, and their separation results are not as expected. In recent years, deep learning technology based on supervised learning has achieved good results in speech separation and other fields, indicating that it is a feasible method.In order to solve the problem of separating source signals from mixed electromagnetic radiation signals and reducing noise interference in electromagnetic safety detection. this paper proposes a Deep Focusing U-Net neural network, which makes the model pay more attention to the features at deeper layer. The network is applied to the blind separation of LCD electromagnetic leakage signals, and the good separation performance proves the effectiveness of this method.

Spaceborne Algorithm for Recognizing Lightning Whistler Recorded by an Electric Field Detector Onboard the CSES Satellite

The electric field detector of the CSES satellite has captured a vast number of lightning whistler events. To recognize them effectively from the massive amount of electric field detector data, a recognition algorithm based on speech technology has attracted attention. However, this approach has failed to recognize the lightning whistler events which are contaminated by other low-frequency electromagnetic disturbances. To overcome this limitation, we apply the single-channel blind source separation method and audio recognition approach to develop a novel model, which consists of two stages. (1) The training stage: Firstly, we preprocess the electric field detector wave data into the audio fragment. Then, for each audio fragment, mel-frequency cepstral coefficients are extracted and input into the long short-term memory network for training the novel lightning whistler recognition model. (2) The inference stage: Firstly, we process each audio fragment with the single-channel blind source to generate two different sub-signals. Then, for each sub-signal, the mel-frequency cepstral coefficient features are extracted and input into the lightning whistler recognition model to recognize the lightning whistler. Finally, the two results above are processed by decision fusion to obtain the final recognition result. Experimental results based on the electric field detector data of the CSES satellite demonstrate the effectiveness of the algorithm. Compared with classical methods, the accuracy, recall, and F1-score of this algorithm can be increased by 17%, 62.2%, and 50%, respectively. However, the time cost only increases by 0.41 s.

A Blind Source Separation Method Based on Bounded Component Analysis Optimized by the Improved Beetle Antennae Search.

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

Multi-domain blind source separation in-band full-duplex technique considering RF impairments

In-band full-duplex (IBFD) technology can potentially improve spectrum efficiency compared to half-duplex technology. The cancellation of the self-interference (SI) signal is the key to implementing the IBFD system to achieve accurate detection of the signal of interest. In this work, we fully account for the effect of IQ imbalance, phase noise (PN), additional Gaussian white noise (AWGN), and the nonlinear caused by the RF impairments of power amplifier, and adopt the method of canceling SI signal combined with the propagation, analog, and digital domain to cancel the SI signal, but the residual SI signal is still mixed with the signal of interest in the same time and frequency domain. In addition, a novel multi-domain blind source separation (MDBSS) self-interference cancellation method implemented by using the off-the-shelf components is proposed in this paper, which can realize the separation of residual self-interference signals from the signal of interest (SOI). Additionally, the multi-domain blind source separation (MDBSS) self-interference cancellation technique is used to achieve 75 dB of self-interference cancellation performance, which is proven through numerical simulation analysis, and the SOI is successfully separated in the receiver. Moreover, the multi-domain blind source separation (MDBSS) method in this paper can be applied to radar, jammer, and communication systems in the future.