An RLS-Iterative Inversion Approach for Blind Signal Separation

This paper investigates the performance of blind signal separation (BSS) algorithms that exploit the temporal predictability of speech. Specifically, the investigation considers how the separation performance of two BSS algorithms will be affected when the length of the AR process (used in the algorithms to model speech) is varied. The investigation concludes that the length of the AR process (prediction order) has a significant impact on separation performance. In particular, the separation performance of both algorithms is degraded, if the AR model’s prediction order, over fits, or under fits, the temporal structure of the speech. It is revealed that a prediction order of 3050 provides maximum separation performance for natural speech, however a prediction order of 10 is more applicable if computational cost is a consideration.

Non-parametric data representation can be done by means of a potential function. This paper introduces a methodology for finding modes of the potential function. Two different methods are considered for the potential function representation: by using summations of Gaussian kernels, and by employing quantum clustering. In the second case each data sample is associated with a quantum physics particle that has a radial energy field around its location. Both methods use a scaling parameter (bandwidth) to model the strength of the influence around each data sample. We estimate the scaling parameter as the mean of the Gamma distribution that models the variances of K-nearest data samples to any given data. The local Hessian is used afterwards to find the modes of the resulting potential function. Each mode is associated with a cluster. We apply the proposed algorithm for blind signal separation and for the topographic segmentation of radar images of terrain.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

Choosing a proper Region of Interest (ROI) for Remote Photoplethysmography (rPPG) is essential and a challenging first step, and it has a direct effect on the accuracy and reliability of the overall heart rate (HR) algorithm. Non-skin areas have no contribution to the HR information; however, few works have tackled the issue of non-skin pixels included in the ROI. First, this paper considers improving the quality of the rPPG signal by filtering out non-skin pixels included within the ROI. The feasibility of employing skin segmentation for ROI definition is demonstrated. Then, this technique is compared with our previous real-time rPPG-based method. Moreover, we explore the effect of extracting the HR from three ROIs using signal fusion. Second, we give a comprehensive account of the examined methods in our algorithm for face detection, face tracking, skin detection, and blind signal separation. Finally, we compare our rPPG measurements with ground truth values obtained from a commercial pulse oximeter. Based on the simulation results, the proposed algorrithm significantly improves the quality of the rPPG technique.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

When the channel wave propagates in the coal seam and encounters an abnormal structure, the frequency, amplitude and other parameters will change. Receiving the channel wave signal clearly and extracting the time-frequency characteristics accurately are the keys to predict the abnormal structure ahead. In this paper, the phased source and new feature extraction method were employed for channel wave seismic exploration. The controllable phased source is used to generate channel wave. The CEEMDAN algorithm based on the EMD algorithm and the Fast ICA algorithm for blind source signal separation are combined new Fast ICA-CEEMDAN method for decomposing the channel wave signal. The homogeneous medium and the equivalent medium model of the mined-out coal measure stratum are established by the COMSOL multiphysics, the received channel wave signal is decomposed by Fast ICA-CEEMDAN method. The Hilbert marginal spectrum, time spectrum, instantaneous frequency spectrum and instantaneous frequency spectrum of channel wave signal were extracted. There are five characteristic parameters of phase spectrum and instantaneous amplitude spectrum. The results show that the method can accurately reflect the local characteristics of the channel wave signal, and has high time-frequency resolution, which provides favorable reference for coal mining.

A cyclostationary-based blind signal separation (BSS) algorithm is proposed in this paper. The proposed separation algorithm is used for simultaneously received multiple spectrum overlapped BPSK signals in single channel electromagnetic surveillance system. The mixed signal is separated by means of blind adaptive frequency shift (BA-FRESH) filter based on the independent signals’ cyclostationary. This BA-FRESH filtering technique does not require training signals. It can separate the independent signals only by knowing some of their cycle frequencies, which is gained by the second order cycle cumulants in this work. Numerical simulation results proved the effectiveness of this separation method.

There are a large variety of applications that require considering sources that usually behave light or strong dependence and this is not the case that common blind signal separation (BSS) algorithms can do. The purpose of this paper is to develop non-parametric BSS algorithm for linear dependent source signals, which is proposed under the framework of contrast method. The contrast function is derived from the Schweizer-Wolff measure of pairwise dependence between the variables. Simulation results show that the proposed algorithm is able to separate the dependent signals and yield ideal performance.

This paper presents a deterministic blind signal separation algorithm. The algorithm assumes transmit diversity, i.e. each user is equipped with at least two transmit antennas. Through each antenna the same symbol sequence is transmitted but coded in a different way. This coding does not require any extra bandwidth or power and allows one to remove multiple access interference (MAI) in a non-iterative way. The algorithm is applicable for any modulation format and forms an extension of the algorithm of Vandaele, Lens and Moonen (see Proceedings of the 2nd IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Annapolis, MD, USA, p.98-101, 1999) which is only applicable for BPSK modulation. We further demonstrate how coding combined with transmit diversity increases robustness to intersymbol interference (ISI) and order detection problems.

Electroencephalography (EEG) has been used extensively for classifying cognitive tasks. Many investigators have demonstrated classification accuracies well over 90% for some combinations of cognitive tasks, signal transformations, and classification methods. Unfortunately, EEG data is prone to significant interference from a wide variety of artifacts, particularly eye blinks. Most methods for classifying cognitive tasks with EEG data simply discard time windows containing eye blink artifacts. However, future applications of EEG-based cognitive task classification should not be hindered by eye blinks. The value of an EEG-controlled human-computer interface, for instance, would be severely diluted if it did not work in the presence of eye blinks. Fortunately, recent advances in blind signal separation algorithms and their applications to EEG data mitigate the artifact contamination issue. In this paper, we show how independent components analysis (ICA) and its extension for sub-Gaussian sources, extended ICA (eICA), can be applied to accurately classify cognitive tasks with eye blink contaminated EEG recordings.

Most of the existing algorithms for blind sources separation have a limitation that sources are statistically independent. However, in many practical applications, the source signals are nonnegative and mutual statistically dependent signals. When the observations are nonnegative linear combinations of nonnegative sources, the correlation coefficients of the observations are larger than these of source signals. In this letter, a novel Nonnegative Matrix Factorization (NMF) algorithm with least correlated component constraints to blind separation of convolutive mixed sources is proposed. The algorithm relaxes the source independence assumption and has low-complexity algebraic computations. Simulation results on blind source separation including real face image data indicate that the sources can be successfully recovered with the algorithm.

An efficient algorithm for blind separation of binary symmetrical signals is proposed, which don’t depend on the statistical characteristics of source signals. The mixture matrix is estimated accurately by using the relations of sensor signals in case of no noise and it is also proved in this paper, and the estimated mixture matrix is a primary column transformation of the original mixture matrix by the algorithm, through which the source signals are recovered by permutations and sign changes of their rows. In practice, they can be corrected by introducing headers in the bit-streams and differently encoding them. The algorithm is shown simple and efficient in last simulations.

This paper proposes a modified natural gradient algorithm (M-NGA) for blind separation of communication signals. In the proposed algorithm, a new cost function is introduced by exploiting both the generalized constant modulus property and the statistical independent property of source signals. Then, the M-NGA is derived by maximizing the new cost function. Simulation results show that the M-NGA has faster convergence than the conventional NGA.

The role of the intelligent optimization algorithm in the blind separation algorithm is to make the independence decision function enter the global optimal position, but the performance of many optimization algorithms depends on the choice of control parameters. In this paper, we use the simplex neighborhood convex set approximation idea, combined with the full random search and multi-role state of the search operator, we use a single-parameter surface-simplex swarm evolution (SSSE), which has a single parameter and pure randomness. The characteristics of the search can overcome the shortcomings of the algorithm’s dependence on the control parameters and easily fall into the local optimum, and improve the application effectiveness of the blind separation algorithm. In the experiment, the fourth-order cumulant (kurtness) is used as the criterion function in ICA, and the improved blind separation algorithm is applied to the blind separation of speech and background music signals. Experimental results show that the improved algorithm effectively separates speech components from the background. The music component and the algorithm have better performance in terms of convergence speed, algorithm stability, and separation effect.

As a new research scope, blind signal separation (BSS) is attracting more and more interests. Independent component analysis (ICA) is an important solution to the problem of BSS, under the assumption conditions of ICA, a BSS algorithm using oblique projection operators is proposed in this paper. The autocorrelation matrix of mixing matrix is used to construct the objective function while the principle of information maximization is adopted to iterate and extract the component, and the mixing matrix can be obtained in a direct way. The description of the problem is demonstrated, and the detailed flow of the proposed method is listed. Simulation results show the suggested scheme is valid even when the weakest signal is less than -80 dB to others.