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Nonparametric adaptive kernel density estimator (NAKDE) based blind source separation(BSS) algorithm is proposed under the framework of natural gradient optimization method. In order to improve the performance of source signal separation by BSS method, the probability distribution functions of source signals must be described as accurately as possible. Compared to the nonparametric fixed-width kernel...
The aim of this paper is to solve the blind source separation (BSS) problem using the temporal independent component analysis (ICA) model. In contrast to ordinary ICA, except for independent assumption, the temporal structure of the source components is taken into account. After combing the virtues of both high order statistics and the temporal second-order information of the source signals, we can...
Nonparametric diffusion mixing estimator (DME) based blind signal separation (BSS) algorithm is proposed under the framework of natural gradient optimization method. In order to improve the performance of signal separation by BSS, the probability distributions of source signals must be described as accurately as possible. In this paper, we use the new data-driven bandwidth selection method based MDE...
The aim of this paper is to solve the blind source separation (BSS) problem using the temporal independent component analysis (ICA) model. In contrast to ordinary ICA, except for independent assumption, the temporal structure of the source components is taken into account. After combing the virtues of both high order statistics and the temporal second-order information of the source signals, we can...
Nonparametric diffusion mixing estimator (DME) based blind signal separation (BSS) algorithm is proposed under the framework of natural gradient optimization method. In order to improve the performance of signal separation by BSS, the probability distributions of source signals must be described as accurately as possible. In this paper, we use the new data-driven bandwidth selection method based MDE...
An adaptive semiblind signal separation algorithm for the separation of a class of arbitrarily distributed but temporally correlated source signals is introduced in this paper. The proposed adaptive algorithm is based only on second-order statistical(SOS) information and exploits the assumption that the source signals are not strictly spatial statistically independent, and temporal do not identical...
Independent component analysis (ICA)/blind source separation (BSS) has received many attentions in neural network and signal processing in recent years. In applications the observed signals are often corrupted with high noise (low SNR, low sample size, non-Gaussian noise), the source number is unknown, and the sources are non-stationary, which are not well correspond to the ideal ICA models and as...
Generalized cross entropy estimator (GCEE) based nonparametric Blind Signal Separation (BSS) algorithm is proposed under the framework of natural gradient optimization method. In order to improve the performance of signal separation by BSS, the probability distribution of source signals must be described as accurately as possible. Compared to the nonparametric fixed-width kernel density estimator...
The purpose of this paper is to develop novel Blind Source Extraction (BSE) algorithms from linear mixtures of the statistically dependent source signals. we show that maximization of the non Gaussianity (NG) measure can not only separate the statistically independent but also dependent source signals. The NG measure is defined by statistical distances between distributions based on the cumulative...
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