Blind Source Extraction for Noisy Mixtures by Combining Gaussian Moments and Generalized Autocorrelations

Authors:
Hongjuan Zhang;Zhenwei Shi;Chonghui Guo;Enmin Feng
Affiliations:
Department of Applied Mathematics, Dalian University of Technology, Dalian, People's Republic of China 116024;Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing, People's Republic of China 100083;Institute of Systems Engineering, Dalian University of Technology, Dalian, People's Republic of China 116024;Department of Applied Mathematics, Dalian University of Technology, Dalian, People's Republic of China 116024
Venue:
Neural Processing Letters
Year:
2008

Citing 18
Cited 0

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
On-line Algorithm for Blind Signal Extraction of Arbitrarily Distributed, but Temporally Correlated Sources Using Second Order Statistics

Neural Processing Letters
Handbook of Image and Video Processing

Handbook of Image and Video Processing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Extraction of Specific Signals with Temporal Structure

Neural Computation
A normalised kurtosis-based algorithm for blind source extraction from noisy measurements

Signal Processing
Letters: Semi-blind source extraction for fetal electrocardiogram extraction by combining non-Gaussianity and time-correlation

Neurocomputing
Filtering by optimal projection and application to automatic artifact removal from EEG

Signal Processing
Fetal cardiac signal extraction from magnetocardiographic data using a probabilistic algorithm

Signal Processing
Topographic independent component analysis based on fractal theory and morphology applied to texture segmentation

Signal Processing
Independent Component Analysis for Time-dependent Processes Using AR Source Model

Neural Processing Letters
Letters: Robust extraction of specific signals with temporal structure

Neurocomputing
Letters: Extraction of a source signal whose kurtosis value lies in a specific range

Neurocomputing
Letters: Gaussian moments for noisy complexity pursuit

Neurocomputing
Sequential blind source separation based exclusively on second-order statistics developed for a class of periodic signals

IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks
From blind signal extraction to blind instantaneous signal separation: criteria, algorithms, and stability

IEEE Transactions on Neural Networks
Blind Source Extraction Using Generalized Autocorrelations

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In the blind source extraction problem, the concept of generalized autocorrelations has been successfully used when the desired signal has special temporal structures. However, their applications are only limited to noise-free mixtures, which is not realistic. Therefore, this paper addresses the extraction of the noisy model based on these temporal characteristics of sources. An objective function, which combines Gaussian moments and generalized autocorrelations, is proposed. Maximizing this objective function, we present a blind source extraction algorithm for noisy mixtures. Simulations on synthesized signals, images, artificial electrocardiogram (ECG) data and the real-world ECG data show the better performance of the proposed algorithm. Moreover, comparisons with the existing algorithms further indicate its validity and also show its robustness to the estimated error of time delay.