Independent vector analysis for convolutive blind noncircular source separation

Authors:
Hefa Zhang;Liping Li;Wanchun Li
Affiliations:
East China Research Institute of Electronic Engineering, Hefei, China and School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China
Venue:
Signal Processing
Year:
2012

Citing 17
Cited 0

Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

Signal Processing
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Independent component analysis by general nonlinear Hebbian-like learning rules

Signal Processing - Special issue on neural networks
High-order contrasts for independent component analysis

Neural Computation
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)

Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Fast fixed-point independent vector analysis algorithms for convolutive blind source separation

Signal Processing
Fixed-point algorithms for the blind separation of arbitrary complex-valued non-Gaussian signal mixtures

EURASIP Journal on Applied Signal Processing
On ICA of improper and noncircular sources

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Blind signal separation and equalization with controlled delay for MIMO convolutive systems

Signal Processing
Review: Independent component analysis for multiple-input multiple-output wireless communication systems

Signal Processing
Nonorthogonal independent vector analysis using multivariate Gaussian model

LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Joint blind source separation by generalized joint diagonalization of cumulant matrices

Signal Processing
Solution of permutation problem in frequency domain ICA, using multivariate probability density functions

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Complex-valued independent vector analysis: Application to multivariate Gaussian model

Signal Processing
On Extending the Complex FastICA Algorithm to Noncircular Sources

IEEE Transactions on Signal Processing
A Unifying Discussion of Correlation Analysis for Complex Random Vectors

IEEE Transactions on Signal Processing
Complex random vectors and ICA models: identifiability, uniqueness, and separability

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.08

Visualization

Abstract

Independent vector analysis (IVA), an extension of independent component analysis (ICA) from univariate components to multivariate components, is a method to tackle blind source separation (BSS) in frequency domain. IVA utilizes both the statistical independence among multivariate signals and the statistical inner dependency of each multivariate signal. However, so far there is no research on IVA for convolutive mixtures of noncircular sources. In this study, we focus on this problem and propose noncircular independent vector analysis (nc-IVA) algorithm, by deriving a new fixed-point algorithm that uses the information of pseudo-covariance matrix in each frequency bin. This modification provides more widely application scenarios with noncircular sources. Simulations demonstrate the effectiveness of our proposed method.