Blind Signal Separation

The linear mixture model (LMM) has recently been used for multi-channel representation of a blurred image. This enables use of multivariate data analysis methods such as independent component analysis (ICA) to solve blind image deconvolution as an instantaneous blind source separation (BSS) requiring no a priori knowledge about the size and origin of the blurring kernel. However, there remains a serious weakness of this approach: statistical dependence between hidden variables in the LMM. The contribution of this paper is an application of the ICA algorithms to the innovations of the LMM to learn the unknown basis matrix. The hidden source image is recovered by applying pseudo-inverse of the learnt basis matrix to the original LMM. The success of this approach is due to the property of the innovations of being more independent and more non-Gaussian than original processes. Our good, consistent simulation and experimental results demonstrate viability of the proposed concept.

In this paper we are interested in developing a new approach that combines successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis. Firstly, vibration signals are pre-processed using successive variational mode decomposition to increase the signal-to-noise ratio. Then, the dynamic time warping algorithm is adopted to select the most effective modes which will be considered as mixture signals. In the second step we apply salp swarm algorithm (SSA) for estimating the de-mixing matrix to extract independent components from mixture signals. However, SSA suffers from the problem of population diversity. Consequently, it offers somewhat different independent sources at every execution of the program. To overcome this shortcoming, the SSA based source estimation will be executed several times with different ranges of initial positions. Then, a fuzzy C-mean algorithm is introduced to select the reliable independent components. The suggested method is tested based on two experiments and compared with other blind source algorithms based on Bat and particle swarm optimization (PSO) algorithms. The obtained results demonstrate the effectiveness of the suggested method in recovering reliable independent components and extracting fault frequency of bearings.

Gravity anomaly is one of efficient methods to evaluate underground structure, which is essential for estimation of ground motion due to earthquakes. Data observation is, however, costly since it requires expensive devices. In order to overcome this problem, Morikawa et al. have been working to develop a mobile gravimeter that uses force-balance (FB) accelerometer. In comparison to the conventional spring type gravimeters, it is less costly, compact and can be carried by relatively small carriers. However, it raised problems that the observed data are severely contaminated by various kinds of disturbances such as engine vibration and carrier motion. Therefore an appropriate data processing method for extracting gravity anomaly signal from such observed data is required. For that purpose, we propose to use the statistical independence property of gravity anomaly and other noisy data. The gravity anomaly and other noises are generated from different sources and it can be safely assumed that they are independent. As a scheme of considering independence of signals, blind source separation techniques are used. Second Order Blind Identification method (SOBI) separates the target sources by assuming that source and noises are un-correlated at various time-lags. Similarly, Independent Component Analysis (ICA) separates the sources by maximizing the independence of linearly transformed observed signals. An ICA algorithm namely ThinICA is proposed that implements the maximization of independence among source signals at various time-lags and thus incorporates the advantages of both SOBI and ICA. The proposed method is applied to the data observed at Toyama Bay, Japan. It is observed that the motion of carrier (ship) influences the performance of de-noising algorithm. Under certain favorable data acquisition environment, the proposed method was able to salvage the gravity anomaly data from the noise-contaminated data with the accuracy sufficient for the purpose of identification of gravity anomaly distribution.

A blind source signal separation problem that was brought to a Study Group in Limerick in 2013 required a way to prevent the gait of a jogger from masking the heartbeat, when detected by a simple photodiode that measures light transmission through a jogger's wrist tissues. The group was successful in discovering a singular value decomposition (SVD) approach, which not only allows accurate detection of heart rate but also allows recovery of a good facsimile of the entire blood pressure time series from the mixed photodiode signal.

Blind Source Separation (BSS) problems, under the assumption of static mixture, were extensively explored from the theoretical point of view. Powerful algorithms are now at hand to deal with many concrete BSS applications. Nevertheless, the performances of BSS methods, for a given biomedical application, are rarely investigated. The aim of this paper is to perform quantitative comparisons between various well-known BSS techniques. To do so, synthetic data, reproducing real polysomnographic recordings, are considered.

We propose a method based on the probabilistic latent component analysis (PLCA) in which we use exponential distributions as priors to decrease the activity level of a given basis vector. A straightforward application of this method is when we try to extract a desired source from a mixture with low artifacts. For this purpose, we propose a maximum a posteriori (MAP) approach to identify the common basis vectors between two sources. A low-artifact estimate can now be obtained by using a constraint such that the common basis vectors in the interfering signal's dictionary tend to remain inactive. We discuss applications of this method in source separation with similar-gender speakers and in enhancing a speech signal that is contaminated with babble noise. Our simulations show that the proposed method not only reduces the artifacts but also increases the overall quality of the estimated signal.

Blind source extraction (BSE) has become one of the promising methods in the field of signal processing and analysis, which only desires to extract "interesting" source signals with specific stochastic property or features so as to save lots of computing time and resources. This paper addresses BSE problem, in which desired source signals have some available reference signals. Based on this prior information, we develop an objective function for extraction of temporally correlated sources. Maximizing this objective function, a semi-blind source extraction fixed-point algorithm is proposed. Simulations on artificial electrocardiograph (ECG) signals and the real-world ECG data demonstrate the better performance of the new algorithm. Moreover, comparisons with existing algorithms further indicate the validity of our new algorithm, and also show its robustness to the estimated error of time delay.

Blind Source Separation (BSS) is a technique used to separate supposed independent sources of signals from a given set of observations. In this paper, the High Exploration Particle Swarm Optimization (HEPSO) algorithm, which is an enhancement of the Particle Swarm Optimization (PSO) algorithm, has been used to separate a set of source signals. Compared to PSO algorithm, HEPSO algorithm depends on two additional operators. The first operator is based on the multi-crossover mechanism of the genetic algorithm while the second one relies on the bee colony mechanism. Both operators have been employed to update the velocity and the position of the particles respectively. Thus, they are used to find the optimal separating matrix. The proposed method enhances the overall efficiency of the standard PSO in terms of good exploration and performance. Based on many tests realized on speech and music signals supplied by the BSS demo, experimental results confirm the robustness and the accuracy of the introduced BSS technique.

Foreground extraction is one of the crucial subjects in image processing, which drives different applications in industry. The reality behind the continuous research in this area is the various challenging problems we encounter during the separation process of foreground and background images. Among the source separation approaches, the independent component analysis (ICA) is the most prevalent, being involved in different areas of signal separation applications. Despite the improvements being achieved in foreground extraction, the sudden luminance variations and background movements adversely impact the results of techniques in this regard. In this paper, a novel structure called HSIC_ICA is introduced to address the mentioned problem using a modified version of the ICA algorithm which, leverages the Hilbert-Schmidt Independence Criterion (HSIC) instead of the common objective functions. Moreover, the unmixing matrix elements of ICA are extracted through a Particle Swarm Optimization (PSO) evolutionary algorithm in a much faster way. The experimental results clearly show that the proposed method outperforms over the significant works being cited among the references, using Wallflower dataset.

This paper links the direct-sequence code-division multiple access (DS-CDMA) multiuser separation-equalization-detection problem to the parallel factor (PARAFAC) model, which is an analysis tool rooted in psychometrics and chemometrics. Exploiting this link, it derives a deterministic blind PARAFAC DS-CDMA receiver with performance close to nonblind minimum mean-squared error (MMSE). The proposed PARAFAC receiver capitalizes on code, spatial, and temporal diversity-combining, thereby supporting small sample sizes, more users than sensors, and/or less spreading than users. Interestingly, PARAFAC does not require knowledge of spreading codes, the specifics of multipath (interchip interference), DOA-calibration information, finite alphabet/constant modulus, or statistical independence/whiteness to recover the information-bearing signals. Instead, PARAFAC relies on a fundamental result regarding the uniqueness of low-rank three-way array decomposition due to Kruskal (and generalized herein to the complex-valued case) that guarantees identifiability of all relevant signals and propagation parameters. These and other issues are also demonstrated in pertinent simulation experiments.

Traditionally, Blind Speech Separation techniques are computationally expensive as they update the demixing matrix at every time frame index, making them impractical to use in many Real-Time applications. In this paper, a robust data driven twomicrophone sound source localization method is used as a criterion to reduce the computational complexity of the Independent Vector Analysis (IVA) Blind Speech Separation (BSS) method. IVA is used to separate convolutedly mixed speech and noise sources. The practical feasibility of the proposed method is proved by implementing it on a smartphone device to separate speech and noise in Real-World scenarios for Hearing-Aid applications. The experimental results with objective and subjective tests reveal the practical usability of the developed method in many real-world applications.

This paper deals with the study of Independent Component Analysis. Independent Component Analysis is basically a method which is used to implement the concept of Blind Source Separation. Blind Source Separation is a technique which is used to extract set of source signal from set of their mixed source signals. The various techniques which are used for implementing Blind Source Separation totally depends upon the properties and the characteristics of original sources. Also there are many fields nowadays in which Independent Component Analysis is widely used. This paper deals with the theoretical concepts of Independent Component Analysis, its principles and its widely used applications.

Vibration sensors have gained increasing popularity as valuable tools for Prognostics and Health Management (PHM) applications, enabling early detection of mechanical failures in industrial machines. Vibration signals comprise two main sources of information: periodic vibrations from components, phase-locked to the rotating speed (e.g., gears), and non-deterministic broadband vibrations associated with bearings, structure, and background noise. In PHM applications, it is important to decompose vibrations to optimize the use of different diagnostic methods for each signal component. In practice, the decomposition should be cost-effective by working without supplementary information about system operating conditions and kinematics. A major application of this method is to separate the bearing and gear signals in a gearbox. This is because the gear signals are typically much stronger than the bearing signals, and can mask the presence of bearing faults. Existing methods of vibration source separation commonly rely on an auto-regression (AR) model of vibrations and employ adaptive filtering techniques to estimate its parameters. However, these methods suffer from degraded accuracy in complex geared vibrations containing numerous periodic components and requiring large filter length to promise high frequency resolution in component separation. To address these challenges, we propose a new method that utilizes dilated Convolutional Neural Networks (CNNs) instead of adaptive filtering to improve the accuracy of decomposing complex vibration signals, all without the need for any supplementary information. To evaluate the performance of the new method, we conducted experiments using both simulated signals and realworld vibrations. The simulation results demonstrate improved accuracy in signal decomposition when our method is used instead of adaptive filtering. Additionally, the new method applied to real vibrations, showcases significant enhancement in bearing failure detection through accurate isolation of bearing-related vibrations. This study reveals the potential of our new method in various PHM applications requiring highly accurate diagnostics and prognostics in complex geared vibrations, particularly when supplementary information about operating conditions and system kinematics is unavailable.

Cloude and Pottier H/α feature space is one of the most employed methods for unsupervised PolSAR data classification based on Incoherent Target Decomposition. The association of the coherence matrix eigenvectors to the most dominant scatters in the analysed pixel introduces unfeasible regions in the H/α plane. The Independent Component Analysis provides promising new information to better interpret non-Gaussian heterogeneous clutter in the frame of polarimetric incoherent target decompositions. Not constrained to any orthogonality between the estimated scattering mechanisms that compose the clutter under analysis, ICA does not introduce any unfeasible region in the H/α plane, increasing the range of possible natural phenomenons depicted in the aforementioned feature space.

This paper proposes an automatic method for artefact removal and noise elimination from scalp electroencephalogram recordings (EEG). The method is based on blind source separation (BSS) and supervised classification and proposes a combination of classical and news features and classes to improve artefact elimination (ocular, high frequency muscle and ECG artefacts). The role of a supplementary step of wavelet denoising (WD) is explored and the interactions between BSS, denoising and classification are analyzed. The results are validated on simulated signals by quantitative evaluation criteria and on real EEG by medical expertise. The proposed methodology successfully rejected a good percentage of artefacts and noise, while preserving almost all the cerebral activity. The "denoised artefact-free" EEG presents a very good improvement compared with recorded raw EEG: 96% of the EEGs are easier to interpret.

This report addresses the problem of estimating complex components from their mixture in the Time-Frequency (TF) domain. Traditional techniques, which consist in non-iteratively optimizing a cost function measuring the difference between the mixture and the model, do not lead to satisfactorily sounding results. Thus, we propose to optimize this cost function by means of an iterative algorithm, which allows us to incorporate some prior phase information in the procedure. We provide a mathematical proof of the nonincreasing property of the error function over the update rules of this algorithm. In addition, we show that the algorithm must be carefully initialized to avoid getting stuck in a local minimum and to output satisfying results.

This paper presents a Bayesian framework for under-determined audio source separation in multichannel reverberant mixtures. We model the source signals as Student's t latent random variables in a time-frequency domain. The specific structure of musical signals in this domain is exploited by means of a non-negative matrix factorization model. Conversely, we design the mixing model in the time domain. In addition to leading to an exact representation of the convolutive mixing process, this approach allows us to develop simple probabilistic priors for the mixing filters. Indeed, as those filters correspond to room responses they exhibit a simple characteristic structure in the time domain that can be used to guide their estimation. We also rely on the Student's t distribution for modeling the impulse response of the mixing filters. From this model, we develop a variational inference algorithm in order to perform source separation. The experimental evaluation demonstrates the potential of this approach for separating multichannel reverberant mixtures.

Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods have become popular in modern data analysis problems due to their computational efficiency. Even though they have proved useful for many statistical models, the application of SG-MCMC to nonnegative matrix factorization (NMF) models has not yet been extensively explored. In this study, we develop two parallel SG-MCMC algorithms for a broad range of NMF models. We exploit the conditional independence structure of the NMF models and utilize a stratified sub-sampling approach for enabling parallelization. We illustrate the proposed algorithms on an image restoration task and report encouraging results.

This paper addresses the problem of multichannel audio source separation in under-determined convolutive mixtures. We target a semi-blind scenario assuming that the mixing filters are known. The convolutive mixing process is exactly modeled using the time-domain impulse responses of the mixing filters. We propose a Student's t time-frequency source model based on non-negative matrix factorization (NMF). The Student's t distribution being heavy-tailed with respect to the Gaussian, it provides some flexibility in the modeling of the sources. We also study a simpler Student's t sparse source model within the same general source separation framework. The inference procedure relies on a variational expectationmaximization algorithm. Experiments show the advantage of using an NMF model compared with the sparse source model. While the Student's t NMF source model leads to slightly better results than our previous Gaussian one, we demonstrate the superiority of our method over two other approaches from the literature.

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In this paper we show that considering early contributions of mixing filters through a probabilistic prior can help blind source separation in reverberant recording conditions. By modeling mixing filters as the direct path plus R-1 reflections, we represent the propagation from a source to a mixture channel as an autoregressive process of order R in the frequency domain. This model is used as a prior to derive a Maximum A Posteriori (MAP) estimation of the mixing filters using the Expectation-Maximization (EM) algorithm. Experimental results over reverberant synthetic mixtures and live recordings show that MAP estimation with this prior provides better separation results than a Maximum Likelihood (ML) estimation.

A great number of methods for multichannel audio source separation are based on probabilistic approaches in which the sources are modeled as latent random variables in a Time-Frequency (TF) domain. For reverberant mixtures, it is common to approximate the time-domain convolutive mixing process as being instantaneous in the short-term Fourier transform domain, under a short mixing filters assumption. The TF latent sources are then inferred from the TF mixture observations. In this paper we propose to infer the TF latent sources from the time-domain observations. This approach allows us to exactly model the convolutive mixing process. The inference procedure relies on a variational expectation-maximization algorithm. In significant reverberation conditions, our approach leads to a signal-to-distortion ratio improvement of 5.5 dB compared with the usual TF approximation of the convolutive mixing process.

Tensor-based analysis of brain imaging data, in particular functional Magnetic Resonance Imaging (fMRI), has proved to be quite effective in exploiting their inherently multidimensional nature. It commonly relies on a trilinear model generating the analyzed data. This assumption, however, may prove to be quite strict in practice; for example, due to the natural intra-subject and inter-subject variability of the Haemodynamic Response Function (HRF). This paper investigates the possible gains from the adoption of a less strict trilinear model, such as PARAFAC2, which allows a more flexible representation of the fMRI data in the temporal domain. In this context, and inspired by a recently reported successful application of the Block Term Decomposition (BTD) model to a 4-way tensorization of the fMRI signal, a PARAFAC2-like extension of BTD (called here BTD2) is proposed. Simulation results are presented, that reveal the pros and cons of these tensorial methods, demonstrating BTD2's enhanced robustness to noise.

Background: The growing interest in neuroimaging technologies generates a massive amount of biomedical data of high dimensionality. Tensor-based analysis of brain imaging data has been recognized as an effective analysis that exploits its inherent multi-way nature. In particular, the advantages of tensorial over matrix-based methods have previously been demonstrated in the context of functional magnetic resonance imaging (fMRI) source localization. However, such methods can also become ineffective in realistic challenging scenarios, involving, e.g., strong noise and/or significant overlap among the activated regions. Moreover, they commonly rely on the assumption of an underlying multilinear model generating the data. -New Method: This paper aims at investigating the possible gains from exploiting the 4-dimensional nature of the brain images, through a higher-order tensorization of the fMRI signal, and the use of less restrictive generative models. In this context, the higher-order Block Term Decomposition (BTD) and the PARAFAC2 tensor models are considered for the first time in fMRI blind source separation. A novel PARAFAC2-like extension of BTD (BTD2) is also proposed, aiming at combining the effectiveness of BTD in handling strong instances of noise and the potential of PARAFAC2 to cope with datasets that do not follow the strict multilinear assumption. -Comparison with Existing Methods: The methods were tested using both synthetic and real data and compared with state of the art methods. -Conclusions: The simulation results demonstrate the effectiveness of BTD and BTD2 for challenging scenarios (presence of noise, spatial overlap among activation regions and inter-subject variability in the Haemodynamic Response Function (HRF)).

In this paper, we propose a supervised multilayer factorization method designed for harmonic/percussive source separation and drum extraction. Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of non negative matrix factorization which is able to exploit time-frequency dictionaries to take into account non stationary drum sounds. The drum dictionaries represent various real drum hits and the decomposition has more physical sense and allows for a better interpretation of the results. Experiments on real music data for a harmonic/percussive source separation task show that our method outperforms other state of the art algorithms. Finally, our method is very robust to non stationary harmonic sources that are usually poorly decomposed by existing methods.

In this paper, we propose a new unconstrained nonnegative matrix factorization method designed to utilize the multilayer structure of audio signals to improve the quality of the source separation. The tonal layer is sparse in frequency and temporally stable, while the transient layer is composed of short term broadband sounds. Our method has a part well suited for tonal extraction which decomposes the signals in sparse orthogonal components, while the transient part is represented by a regular nonnegative matrix factorization decomposition. Experiments on synthetic and real music data in a source separation context show that such decomposition is suitable for audio signal. Compared with three state-of-the-art harmonic/percussive decomposition algorithms, the proposed method shows competitive performances.

The extraction of a desired speech signal from a noisy environment has become a challenging issue. In the recent years, the scientific community has particularly focused on multichannel techniques which are dealt with in this review. In fact, this study tries to classify these multichannel techniques into three main ones: Beamforming, Independent Component Analysis (ICA) and Time Frequency (T-F) masking. This paper also highlights their advantages and drawbacks. However these previously mentioned techniques could not afford satisfactory results. This fact leads to the idea that a combination of those techniques, which is depicted along this study, may probably provide more efficient results. Indeed, giving the fact that those approaches are still be considered as being not totally efficient, has led us to review these mentioned above in the hope that further researches will provide this domain with suitable innovations.

In this paper, we exploit the symmetry properties of fourthorder cumulants to develop a new blind identification algorithm for multiple-input multiple-output (MIMO) instantaneous channels. The proposed algorithm utilizes the Parallel Factor (Parafac) decomposition of the 4th-order cumulant tensor by solving a single-step (SS) least squares (LS) problem. This approach is shown to hold for chan- nels with more sources than sensors. A simplified approach using a reduced-order tensor is also discussed. Computer simulations are provided to illustrate the performance of the proposed identification algorithms.

We propose a method for source separation of convolutive mixture based on nonlinear prediction-error filters. This approach converts the original problem into an instantaneous mixture problem, which can be solved by any of the several existing methods in the literature. We employ fuzzy filters to implement the prediction-error filter, and the efficacy of the proposed method is illustrated by some examples.

Independent component analysis (ICA) is a very popular method that has shown success in blind source separation, feature extraction and unsupervised recognition. In recent years ICA has been largely studied by researchers from the signal processing community. This paper addresses a more in-depth study on the use of this method, applied to hyperspectral images used for remote sensing purposes. In a first part, source separation is addressed. Since the independence of sources is usually not verified in hyperspectral real data images, ICA, if used alone, is not a suitable tool to unmix sources. We propose a hierarchical approximation for the use of ICA as a pre-processing step for a Bayesian Positive Source Separation method. In a second part, the use of ICA for dimensionality reduction is studied in the frame of hyperspectral data classification. Experimental results show the effectiveness of ICA when used for hyperspectral image pre-processing for the two considered applications.

The surface of Mars is currently being imaged with an unprecedented combination of spectral and spatial resolution. This high resolution, and its spectral range, give the ability to pinpoint chemical species on the surface and the atmosphere of Mars moreaccurately than before. The subject of this paper is to present a method to extract informations on these chemicals from hyperspectral images. A first approach, based on Independent Component Analysis (ICA) , is able to extract artifacts and locations of CO2 and H2O ices. However, the main independence assumption and some basic properties (like the positivity of images and spectra) being unverified, the reliability of all the independent components (ICs) is weak. For improving the component extraction and consequently the endmember classification, a combination of spatial ICA with spectral Bayesian Positive Source Separation (BPSS) [2] is proposed. To reduce the computational burden, the basic idea is to use spatial ICA yielding a rough classification of pixels, which allows selection of small, but relevant, number of pixels and then BPSS is applied for the estimation of the source spectra using the spectral mixtures provided by this reduced set of pixels. Finally, the abundances of the components is assessed on the whole pixels of the images. Results of this approach are shown and evaluated by comparison with available reference spectra.

This paper proposes a 2D Non-negative Matrix Factorization (NMF) based single-channel source separation algorithm that emphasizes perceptually important components of audio. Unlike the existing methods, the proposed scheme performs a psychoacoustic pre-processing on the mixture spectrogram in order to supress audio components that are not critical to human hearing sensation while amplifying the perceptually important ones. This yields the auditory spectrogram referred as sonogram of the observed audio mixture and the individual sources are then extracted by 2D NMF. Test results reported in terms of Signal-to-Distortion-Ratio (SDR), Signalto-Inference-Ratio (SIR) and Signal-to-Artifact-Ratio (SAR) show that the proposed perceptually enhanced separation improves the quality of decomposed audio sources by 1.5-6.5 dB with a reduced computational complexity.

We propose a Non-Intrusive (or reference-free) Audio Clarity index (NIAC), inspired from previous works on image sharpness and defined as the sensitivity of the spectrogram sparsity to a convolution of the audio signal with a white noise. A closed-form formula is provided, which only involves the signal itself and very little parameter setting. Tested in various noise and reverberation conditions, the NIAC exhibits a high correlation with the well-established Speech Transmission Index, both for speech and music. It can also be used as a clarity criterion to drive sound enhancement algorithms. We propose a NIAC-based source separation algorithm, and show that its performance is comparable to that of a state-of-the-art algorithm, FastICA.

We propose a Non-Intrusive (or reference-free) Audio Clarity index (NIAC), inspired from previous works on image sharpness and defined as the sensitivity of the spectrogram sparsity to a convolution of the audio signal with a white noise. A closed-form formula is provided, which only involves the signal itself and very little parameter setting. Tested in various noise and reverberation conditions, the NIAC exhibits a high correlation with the well-established Speech Transmission Index, both for speech and music. It can also be used as a clarity criterion to drive sound enhancement algorithms. We propose a NIAC-based source separation algorithm, and show that its performance is comparable to that of a state-of-the-art algorithm, FastICA.

This paper deals with direction of arrival (DOA) estimation and blind signal separation (BSS) based on independent component analysis (ICA) with robust capabilities. An efficient demixing procedure of complex-valued ICA is presented here, which combines the signal-subspace demixing procedure exploiting individual signal-subspace projection and Newton's iteration algorithm based on maximization of the approximate negentropy of non-Gaussian signal for array signal processing. It resolves the problems of order ambiguity and identifiability of traditional ICA for time-domain BSS. The proposed method could be directly applied to radar, sonar, radio surveillance, and communications systems for separating signals and estimating relative DOAs of signals. Several computer simulation examples for perturbations to the array manifold, unknown noise environments, and Rayleigh fading channel are provided to illustrate the effectiveness of the proposed method.

Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal blind source separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e., the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy. Methods: Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequencydomain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Database. The performance of the subsequent component selection was evaluated by means of the heartbeat detection accuracy (ACC) using an automatically selected single component. Results: The proposed heartbeat-detection-based selection routines significantly outperformed the standard selectors based on Skewness, Kurtosis, and frequency-domain features, especially for datasets containing motion artifacts. For arrhythmia data, beat analysis by sparse coding outperformed simple periodicity tests of the detected heartbeats. Conclusion: Component selection routines based on heartbeat detections are capable of reliably selecting cardiac signals after spatio-temporal BSS in case of severe motion artifacts and arrhythmia. Significance: The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatiotemporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.

Camera-based photoplethysmography is a contactless mean to assess vital parameters, such as heart rate and respiratory rate. In the field of camera-based photoplethysmography, blind source separation (BSS) techniques have been extensively applied to cope with artifacts and noise. Despite their wide usage, there is no consensus that common BSS approaches contribute to an improved analysis of camera-based photoplethysmograms (cbPPG). This contribution compares previously proposed multispectral BSS techniques to a novel spatial BSS approach for heart rate extraction from cbPPG. Our analysis indicates that the application of BSS techniques not necessarily improves cbPPG's analysis but signal properties like the signal-to-noise-ratio should be considered before applying BSS techniques.

Wearable sensor technology like textile electrodes provides novel ambulatory health monitoring solutions but most often goes along with low signal quality. Blind Source Separation (BSS) is capable of extracting the Electrocardiogram (ECG) out of heavily distorted multichannel recordings. However, permutation indeterminacy has to be solved, i.e. the automated selection of the desired BSS output. To that end we propose to exploit the sparsity of the ECG modeled as a spike train of successive heartbeats. A binary code derived from a two-item dictionary {peak, no peak} and physiological a-priori information temporally represents every BSS output component. The (best) ECG component is automatically selected based on a modified Hamming distance comparing the components' code with the expected code behavior. Non-standard ECG recordings from ten healthy subjects performing common motions while wearing a sensor garment were subsequently processed in 10 s segments with spatio-temporal BSS. Our sparsity-based selection RCODE achieved 98.1% heart beat detection accuracy (ACC) by selecting a single component each after BSS. Traditional component selection based on higherorder statistics (e.g. skewness) achieved only 67.6% ACC.

Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal Blind Source Separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e. the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy. Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequency-domain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Databa...

Blind source separation (BSS) aims at separating useful signal content from distortions. In the contactless acquisition of vital signs by means of the camera-based photoplethysmogram (cbPPG), BSS has evolved the most widely used approach to extract the cardiac pulse. Despite its frequent application, there is no consensus about the optimal usage of BSS and its general benefit. This contribution investigates the performance of BSS to enhance the cardiac pulse from cbPPGs in dependency to varying input data characteristics. The BSS input conditions are controlled by an automated spatial preselection routine of regions of interest. Input data of different characteristics (wavelength, dominant frequency, and signal quality) from 18 postoperative cardiovascular patients are processed with standard BSS techniques, namely principal component analysis (PCA) and independent component analysis (ICA). The effect of BSS is assessed by the spectral signal-tonoise ratio (SNR) of the cardiac pulse. The preselection of cbPPGs, appears beneficial providing higher SNR compared to standard cbPPGs. Both, PCA and ICA yielded better outcomes by using monochrome inputs (green wavelength) instead of inputs of different wavelengths. PCA outperforms ICA for more homogeneous input signals. Moreover, for high input SNR, the application of ICA using standard contrast is likely to decrease the SNR.

Camera-based photoplethysmography is a contactless mean to assess vital parameters, such as heart rate and respiratory rate. In the field of camera-based photoplethysmography, blind source separation (BSS) techniques have been extensively applied to cope with artifacts and noise. Despite their wide usage, there is no consensus that common BSS approaches contribute to an improved analysis of camera-based photoplethysmograms (cbPPG). This contribution compares previously proposed multispectral BSS techniques to a novel spatial BSS approach for heart rate extraction from cbPPG. Our analysis indicates that the application of BSS techniques not necessarily improves cbPPG's analysis but signal properties like the signal-to-noise-ratio should be considered before applying BSS techniques.

This paper proposes a new method to solve the post-nonlinear blind source separation problem (PNLBSS). The method is based on the fact that the distribution of the output signals of the linearly mixed system are approximately Gaussian distributed. According to the central limit theory, if one can manage the probability density function (PDF) of the nonlinear mixed signals to be Gaussian distribution, then this means that the signals becomes linearly mixed in spite of the PDF of its separate components. In this paper, the short time Gaussianization utilizing the B-spline neural network is used to ensure that the distribution of the signal is converted to the Gaussian distribution. These networks are built using neurons with flexible B-spline activation functions. The fourth order moment is used as a measurement of Gaussianization. After finishing the Gaussianization step, linear blind source separation method is used to recover the original signals. Performed computer simulations have shown the effectiveness of the idea, even in presence of strong nonlinearities and synthetic mixture of real world data.

This paper presents an accurate nonparametric method for evaluating signal's probability density function (pdf), as well as its entropy. It is based on using Bspline wavelets, as the smoothing filter for the data histogram distribution. Due to the excellent energy concentration feature of Bspline wavelets, this estimation was found to be accurate and robust of probability density function (pdf) estimation that are needed in some linear blind source separation (BSS) designs. The validity of the proposed technique is checked by its ability of recovering linearly blind source BSS, with a simple check to verify exact source recovery when no information is available about the mixing system. Several experiments have been carried out, to verify the ability of the proposed technique to accurately estimating signal's pdf as well as recovering linearly mixed signals and images with a simple independency check to determine whether exact separation is achieved or not.

In this paper, a new technique to solve the nonlinear blind source separation problem (NBSS) is introduced. The method is based on the concept of reducing the high frequency component of the nonlinear mixed signal by dividing the mixed signal into blocks in the time domain, with any arbitrary size. To remove the distortion of the nonlinear function, the discreet cosine transform (DCT) is applied on each block. By adaptively adjusting the size of the DCT block of data, the highly correlated subblocks, can be estimated, then the correlation between the highly correlated sub-blocks can be reduced. To complete the separation process, the linear blind source separation (BSS) algorithm based on the wavelet transform is used to reduced the correlation between the highly correlated DCT subblock. Performed computer simulations have shown the effectiveness of the idea, even in presence of strong nonlinearities and synthetic mixture of real world data (like speech and image signals).

We revisit the standard differential microphone of [1] and propose three algorithmic enhancements. First, a simple alignment procedure equalizes the power levels of the input signals. Second, we describe an efficient algorithm to equalize the distorted directional response of the original algorithm. This enables a wider operating frequency range and helps to reduce the noise gain of the array. Third, a fast time-domain notch adaptation algorithm is presented to reliably track interfering sound sources. Its tracking performance is not impaired by the activity of desired sound sources. Finally, we describe a fixed-point arithmetic implementation of the modified algorithm for wideband handsfree communication ( f s = 16 kHz) using two miniature digital MEMS microphone capsules.

The problem of undecided Separating reverberant audio sources is crucial for speech and audio processing. Numerous separation strategies have been developed to solve this problem; however, all of them estimate model parameters in the time-frequency domain, resulting in permutation ambiguity and poor separation performance. Additionally, one of the main challenges with existing expectation-maximization (EM) strategies is the time needed for each iterative step to update the model parameters. In this article, we offer an enhanced EM approach that combines nonnegative matrix factorization (NMF) with time differences of arrival (TDOA) estimations while eliminating time expenditure to the EM algorithm's starting values being appropriately selected. The suggested approach avoids permutation ambiguity by using the NMF source model, and acoustic localization is accomplished by converting the TDOA. Following that, model parameters are changed to improve separation outcomes. Finally, Wiener filters are used to separate the source signals. The experimental findings indicate that the suggested algorithm outperforms current blind separation approaches in terms of source separation.

Fetal heart monitoring yields vital information about the fetus health and can support medical decision making in critical situations. A compound signal is obtained noninvasively by placing electrodes on the abdomen area of the mother which contains maternal and fetal ECG signals contaminated by various other signals from body and externally induced noises. As a result, the basic problem is to extract the fECG signal from the mixture of mECG and fECG, where the interfering mECG is a much stronger .This problem has been addressed in several works, in this paper, we propose a novel blind-source separation method to extract fetal ECG when given only a single channel recording .Our approach is based on the wavelet theory and SVD-ICA methods. It is pointed out that the presented algorithm is validated by Matlab simulation and can be implemented on real time life by using embedded system such as DSP processor.