Blind Separation of Complex Sources Using Generalized Generating Function

A novel blind separation approach using higher-order time-frequency distributions is presented. The concept of higher-order time-frequency distribution matrix is also introduced. It is devised to primarily separate sources with temporal nonstationary signal characteristics. So far, this problem has been solved using statistical information available on the source signals. In contrast to well known blind source separation approaches using second-order statistics (SOS) and/or higher-order statistics (HOS) which rely on stationarity properties of the signals, the proposed approach allows separation of the sources with nonstationarity properties. In addition, the effect of spreading the noise power while localizing of the source energy in the time-frequency domain amounts to increasing the signal to noise ratio. A computationally feasible implementation is presented based on joint diagonalization of the matrices of the principal slices of the time-multifrequency domain of support of the cumulant-based Wigner trispectrums. Numerical examples demonstrate the effectiveness of the proposed approach.

Indoor vehicle pass-by noise applications deal with measuring the exterior noise from a vehicle fixed on a chassis dynamometer in a large hemi-anechoic room. During a standardised acceleration test, the noise is measured with an array of microphones placed in the far-field, and the overall noise level versus vehicle position can be simulated. The indoor facility allows controlled and repeatable measurements independent of weather. For engineering purposes, pass-by contribution analysis can be included in the test leading to information about the pass-by noise contribution from major noise sources. This work presents a novel application of blind source separation to vehicle measurements from an indoor pass-by measurement campaign. In contrast to the classical transfer path approach using point sources for modelling vehicle noise sources and combining an operational measurement with transfer functions, the blind approach does not consider a specific noise source model. It only assumes that the noise is produces by a set of independent noise sources using only a single operational measurement for a given vehicle condition as input. Near-field microphone measurements are blindly separated into independent components and further correlated with the signals measured at the far-field indoor pass-by microphones to get the time-domain contributions. Finally, we apply the indoor simulated pass-by algorithm to produce noise contribution levels as a function of vehicle position. We discuss the specified application of blind source separation to vehicle measurements for different operating conditions from a real indoor pass-by test. Separation of tyre and engine related noise at tyre near-field microphones is verified. Furthermore, the tyre pass-by noise contribution is extracted from the overall vehicle measurement.

ERP is commonly obtained by averaging over segmented EEC epochs. In case artifacts are present in the raw EEC measurement, pre-processing is required to prevent the averaged ERP waveform being interfered by artifacts. The simplest pre-processing approach is by rejecting trials in which presence of artifact is detected. Alternatively artifact correction instead of rejection can be performed by blind source separation, so that waste of ERP trials is avoided. In this paper, we propose a second order statistics based blind source separation approach to ERP artifact correction. Comparing with blind separation using independent component analysis, second order statistics based method does not rely on higher order statistics or signal entropy, and therefore leads to more robust separation even if only short epochs are available.

Blind Signal Separation II. Linear, Convolutive Combinations: II. Linear, Convolutive Combinations

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.

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.

Most of the HO blind source separation methods developed this last decade aim at blindly separating statistically independent sources, assumed stationary and ergodic. Nevertheless, in many situations such as in radiocommunications, the sources are non-stationary and very often (quasi)-cyclostationary (digital modulations). In these contexts, it is important to wonder if the performance of these HO blind source separation methods may be affected by the potential non-stationarity of the sources. The purpose of this paper is to bring some answers to this question through the behaviour analysis of the empirical fourth-order cumulant estimators in the presence of (quasi)-cyclostationary sources.

Blind separation of non-stationary sources using continuous density hidden Markov models

This paper presents a new approach for blind separation of nonstationary frequency-modulated (FM) sources in the underdetermined case (i.e., more sources than sensors) using their time-frequency distributions (TFDs). The underlying idea of the proposed blind source separation (BSS) method is based on the observation that a monocomponent FM signal is represented by a linear feature corresponding to the 'energy concentration points' in the time-frequency (TF) image. Therefore, we propose to adapt an existing 'road network extraction' method [Tupin et al., (1998)] for the detection and separation of the source signal components from the spatially averaged TF image of their mixtures. The sources spatial signatures are then used to group together (classify) the components of the same source (or equivalently, the same spatial direction). Simulation examples are provided to assess the performance of the proposed algorithm in various scenarios.

In the case of multiple nonstationary independent source signals and linear instantaneous time-varying mixing systems, it is difficult to adaptively separate the multiple source signals. Therefore, the adaptive blind source separation (BSS) problem is firstly formally expressed and compared with tradition BSS problem. Then, we propose an adaptive blind identification and separation method based on the variable learning rate equivariant adaptive source separation via independence (EASI) algorithm. Furthermore, we analyze the scope and conditions of variable-learning rate EASI algorithm. The adaptive BSS simulation results also show that the variable learning rate EASI algorithm provides better separation effect than the fixed learning rate EASI and recursive least-squares algorithms.

In this paper, a new approach for blind source separation (BSS) based on blind localization is presented, which makes use of the inherent relationship between separation and localization. The approach incorporates the advantages of classical algorithms of BSS and localization. It can not only determine the number of signal sources, which is difficult to deal with by traditional separation algorithms, but also reduce the adaptive learning time. The efficiency of the approach is illustrated by simulation.

Discrimination between normal sinus rhythm (NSR), atrial fibrillation (AF) and atrial flutter (AFL) using the surface electrocardiogram (ECG) through direct Fourier analysis is not effective because ventricular activity (VA) overlaps in frequency with atrial activity (AA) and, moreover, AA frequency components present considerably lower amplitude than VA. This contribution exploits the assumption that AA and VA can be considered as generated by independent sources of cardioelectric activity. Hence, AA free from spurious VA and other disturbances can be extracted through the application of blind source separation (BSS) techniques. After the blind separation and AA identification process, it was applied a logistic regression over a group of spectral parameters extracted from the power spectral density of the AA signal that was able to discriminate between AF, AFL and NSR with a 100% accuracy considering only two spectral parameters.

The technique of blind source separation (BSS) has been well studied. In this paper, we apply the BSS technique, particularly based on independent component analysis (ICA), to a meeting situation. The goal is to enhance the spoken utterances and to estimate the location of each speaker by means of multiple microphones. The technique may help us to take the minutes of a meeting.

Upgrading Sparse NMF algorithm for blind source separation through Adaptive Parameterized Hybrid Kernel based approach

Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices