Mixing vector construction for single channel semi-blind source separation using empirical mode decomposition

Miao, S, Hou, J, Wang, W and Yao, S 2014, Mixing vector construction for single channel semi-blind source separation using empirical mode decomposition, in ICSP2014: Proceedings of 2014 IEEE 12th International Conference on Signal Processing, IEEE, Piscataway, N.J., pp. 22-27.

Attached Files
Name Description MIMEType Size Downloads

Title Mixing vector construction for single channel semi-blind source separation using empirical mode decomposition
Author(s) Miao, S
Hou, JORCID iD for Hou, J orcid.org/0000-0002-6403-9786
Wang, W
Yao, S
Conference name IEEE International Conference on Signal Processing (12th : 2014 : HangZhou, China)
Conference location HangZhou, China
Conference dates 19-23 Oct. 2014
Title of proceedings ICSP2014: Proceedings of 2014 IEEE 12th International Conference on Signal Processing
Editor(s) Yuan, B.
Ruan, Q.
Tang, X.
Publication date 2014
Conference series IEEE International Conference on Signal Processing
Start page 22
End page 27
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Empirical Mode Decomposition (EMD)
Single Channel Blind Source Separation (SCBSS)
Mixing Vector
Adaptive Filter
Summary The Empirical Mode Decomposition (EMD) method is a commonly used method for solving the problem of single channel blind source separation (SCBSS) in signal processing. However, the mixing vector of SCBSS, which is the base of the EMD method, has not yet been effectively constructed. The mixing vector reflects the weights of original signal sources that form the single channel blind signal source. In this paper, we propose a novel method to construct a mixing vector for a single channel blind signal source to approximate the actual mixing vector in terms of keeping the same ratios between signal weights. The constructed mixing vector can be used to improve signal separations. Our method incorporates the adaptive filter, least square method, EMD method and signal source samples to construct the mixing vector. Experimental tests using audio signal evaluations were conducted and the results indicated that our method can improve the similar values of sources energy ratio from 0.2644 to 0.8366. This kind of recognition is very important in weak signal detection.
ISBN 9781479921867
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080110 Simulation and Modelling
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Institute of Electrical and Electronics Engineers (IEEE), Inc.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30069458

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 293 Abstract Views, 7 File Downloads  -  Detailed Statistics
Created: Fri, 13 Mar 2015, 16:46:19 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.