Results 151 to 160 of about 5,520,700 (190)

Complex Blind Source Separation

Circuits, Systems, and Signal Processing, 2017
Blind source separation (BSS) techniques aim at recovering the original source signals from observed mixtures without a priori information. The bivariate empirical mode decomposition (BEMD) algorithm combined with complex independent component analysis by entropy bound minimization (ICA-EBM) technique is proposed as an alternative to separate ...
Mina Kemiha, Abdellah Kacha
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Blind source separation: a unified approach

Neurocomputing, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yogesh Singh, Chandra Shekhar Rai
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General Approach to Blind Source Separation

IEEE Transactions on Signal Processing, 1996
This paper identifies and studies two major issues in the blind source separation problem: separability and separation principles. We show that separability is an intrinsic property of the measured signals and can be described by the concept of m-row decomposability introduced in this paper; we also show that separation principles can be developed by ...
Xi-Ren Cao, Ruey-Wen Liu
exaly   +2 more sources

Proportionate Algorithms for Blind Source Separation

2014
In this paper we propose an extension of time-domain Blind Source Separation algorithms by applying the well known proportionate and improved proportionate adaptive algorithms. These algorithms, known in the context of adaptive filtering, are able to use the sparseness of acoustic impulse responses of mixing environments and give better performances ...
SCARPINITI, MICHELE, COMMINIELLO, DANILO, SCARDAPANE, SIMONE, PARISI, Raffaele, UNCINI, Aurelio   +4 more
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Source distribution models for blind source separation

Neurocomputing, 2004
Abstract Various methods have been proposed to separate mixtures of sub- and super-Gaussian signals. Effectiveness of a blind source separation algorithm depends upon the source distribution model used for deriving the weight update rule. Different hypothesized source distribution models are used for representing sub- and super-Gaussian sources.
Chandra Shekhar Rai, Yogesh Singh
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Blind source separation in frequency domain

Signal Processing, 2003
A blind source separation method based on cross-correlation in frequency domain is performed in this paper. Firstly, the pseudo-solution is described and possible treatment is proposed. Then the oscillation of the un-mixing matrix is proved to be the basic aspect that degrades the separation performance for larger frame size.
Yi Zhou, Boling Xu
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Effective blind separation of skewed sources

Signal Processing, 2006
This article proposes a new algorithm for estimating skewed real signals in blind source separation. It is based on the eigendecomposition of a third-order statistics matrix. This matrix is updated iteratively by the algorithm in order to increase the robustness of the estimated source signals against numerical errors and perturbations.
Rubén Martín-Clemente, Susana Hornillo-Mellado   +1 more
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Novel Approach for Blind Source Separation

2013 Sixth International Conference on Developments in eSystems Engineering, 2013
An attempt has been made to use efficient Neuron model for blind source separation. Generalized Harmonic Mean Neuron (GHMN) has been used as the neuron model. GHMN model is based on generalized harmonic mean of the inputs applied on it. Information-maximization approach has been used for training the neuron model.
Md. Shiblee, B. Chandra 0001
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Enhancement of Source Independence for Blind Source Separation

2006
When exploiting independent component analysis (ICA) to perform blind source separation (BSS), it is assumed that sources are mutually independent. However, in practice, the latent sources are usually dependent to some extent. Fortunately, if the sources are the same type of natural signals, they may be mutually independent in some frequency band, and ...
Kun Zhang 0001, Lai-Wan Chan
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Blind Source Separation

2019
Blind source separation algorithms have been widely used in the EEG signal processing. This chapter introduces the EEG model basis of blind source separation and details of three mainstream algorithms, i.e., principal component analysis (PCA), independent component analysis (ICA), and tensor decomposition, to provide a comprehensive review on this ...
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