Source separation using single channel ICA

Authors:
M. E. Davies;C. J. James
Affiliations:
IDCOM and the Joint Research Institute for Signal and Image Processing, The University of Edinburgh, Scotland, UK;Signal Processing and Control Group, ISVR, University of Southampton, UK
Venue:
Signal Processing
Year:
2007

Citing 15
Cited 2

Extracting qualitative dynamics from experimental data

Physica D
Fundamentals of statistical signal processing: estimation theory

Fundamentals of statistical signal processing: estimation theory
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Can we learn anything from single-channel unaveraged MEG data?

Neural Computing and Applications
Single channel speech enhancement by efficient coding

Signal Processing
Topographic Independent Component Analysis

Neural Computation
A Variational Method for Learning Sparse and Overcomplete Representations

Neural Computation
Learning Overcomplete Representations

Neural Computation
On the analysis of single versus multiple channels of electromagnetic brain signals

Artificial Intelligence in Medicine
Sparse coding for convolutive blind audio source separation

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Independent component analysis for speech enhancement with missing TF content

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Extracting multisource brain activity from a single electromagnetic channel

Artificial Intelligence in Medicine
New criteria for blind deconvolution of nonminimum phase systems (channels)

IEEE Transactions on Information Theory
On the asymptotic eigenvalue distribution of Toeplitz matrices

IEEE Transactions on Information Theory

Space-time ICA and EM brain signals

ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A new single-mixture source separation method

International Journal of Computer Applications in Technology

Quantified Score

Hi-index	0.08

Visualization

Abstract

Many researchers have recently used independent component analysis (ICA) to generate codebooks or features for a single channel of data. We examine the nature of these codebooks and identify when such features can be used to extract independent components from a stationary scalar time series. This question is motivated by empirical work that suggests that single channel ICA can sometimes be used to separate out important components from a time series. Here we show that as long as the sources are reasonably spectrally disjoint then we can identify and approximately separate out individual sources. However, the linear nature of the separation equations means that when the sources have substantially overlapping spectra both identification using standard ICA and linear separation are no longer possible.