Edge effect elimination in single-mixture blind source separation

Guo, Y; Huang, S; Li, Y; Naik, GR

Edge effect elimination in single-mixture blind source separation

Guo, Y Huang, S Li, Y Naik, GR

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Publication Type:: Journal Article
Citation:: Circuits, Systems, and Signal Processing, 2013, 32 (5), pp. 2317 - 2334
Issue Date:: 2013-10-01

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Field	Value	Language
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Huang, S	en_US
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.date.issued	2013-10-01	en_US
dc.identifier.citation	Circuits, Systems, and Signal Processing, 2013, 32 (5), pp. 2317 - 2334	en_US
dc.identifier.issn	0278-081X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26646
dc.description.abstract	Blind source separation (BSS) of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Ensemble empirical mode decomposition (EEMD)-based methods are commonly used for blind separation of single input multiple outputs. However, all of these EEMD-based methods appear in the edge effect problem when cubic spline interpolation is used to fit the upper and lower envelopes of the given signals. It is therefore imperative to have good methods to explore a more suitable design choice, which can avoid the problems mentioned above as much as possible. In this paper we present a novel single-mixture blind source separation method based on edge effect elimination of EEMD, principal component analysis (PCA) and independent component analysis (ICA). EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). In extreme point symmetry extension (EPSE), optimum values of endpoints are obtained by minimizing the deviation evaluation function of signal and signal envelope. Edge effect is turned away from signal by abandoning both ends' extension parts of IMFs. PCA is applied to reduce dimensions of IMFs. ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction of IMFs. The separated performance of edge EPSE-EEMD-PCA-ICA algorithm is compared with EEMD-ICA and EEMD-PCA-ICA algorithms through simulations, and experimental results show that the former algorithm outperforms the two latter algorithms with higher correlation coefficient and lower relative root mean square error (RRMSE). © 2013 Springer Science+Business Media New York.	en_US
dc.relation.ispartof	Circuits, Systems, and Signal Processing	en_US
dc.relation.isbasedon	10.1007/s00034-013-9556-9	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Edge effect elimination in single-mixture blind source separation	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	32	en_US
utslib.for	090609 Signal Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

Blind source separation (BSS) of single-channel mixed recording is a challenging task that has applications in the fields of speech, audio and bio-signal processing. Ensemble empirical mode decomposition (EEMD)-based methods are commonly used for blind separation of single input multiple outputs. However, all of these EEMD-based methods appear in the edge effect problem when cubic spline interpolation is used to fit the upper and lower envelopes of the given signals. It is therefore imperative to have good methods to explore a more suitable design choice, which can avoid the problems mentioned above as much as possible. In this paper we present a novel single-mixture blind source separation method based on edge effect elimination of EEMD, principal component analysis (PCA) and independent component analysis (ICA). EEMD represents any time-domain signal as the sum of a finite set of oscillatory components called intrinsic mode functions (IMFs). In extreme point symmetry extension (EPSE), optimum values of endpoints are obtained by minimizing the deviation evaluation function of signal and signal envelope. Edge effect is turned away from signal by abandoning both ends' extension parts of IMFs. PCA is applied to reduce dimensions of IMFs. ICA finds the independent components by maximizing the statistical independence of the dimensionality reduction of IMFs. The separated performance of edge EPSE-EEMD-PCA-ICA algorithm is compared with EEMD-ICA and EEMD-PCA-ICA algorithms through simulations, and experimental results show that the former algorithm outperforms the two latter algorithms with higher correlation coefficient and lower relative root mean square error (RRMSE). © 2013 Springer Science+Business Media New York.

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http://hdl.handle.net/10453/26646