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Reseach Article

Blind Audio Source Separation in Time Domain using ICA Decomposition

by Naveen Dubey, Rajesh Mehra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 6
Year of Publication: 2015
Authors: Naveen Dubey, Rajesh Mehra
10.5120/ijca2015907532

Naveen Dubey, Rajesh Mehra . Blind Audio Source Separation in Time Domain using ICA Decomposition. International Journal of Computer Applications. 132, 6 ( December 2015), 48-53. DOI=10.5120/ijca2015907532

@article{ 10.5120/ijca2015907532,
author = { Naveen Dubey, Rajesh Mehra },
title = { Blind Audio Source Separation in Time Domain using ICA Decomposition },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 6 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 48-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number6/23602-2015907532/ },
doi = { 10.5120/ijca2015907532 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:28:28.586048+05:30
%A Naveen Dubey
%A Rajesh Mehra
%T Blind Audio Source Separation in Time Domain using ICA Decomposition
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 6
%P 48-53
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Algorithms for Blind Audio Source Separation (BASS) in time domain can be categories as based on complete decomposition or based on complete decomposition. Partial decomposition of observation space leads to additional computational complexity and burden, to minimize resource requirement complete decomposition technique is preferred. In this script an optimized divergence based ICA technique is proposed to perform ICA decomposition. After decomposition components having similar behaviour are grouped in form of clusters and source signals are reconstructed. The authors implemented complete decomposition for BASS using ICA methods and K-mean cluster technique is introduced. For performance evaluation a three source and three microphones combination is used and result advocates complete decomposition by optimized ICA is a better option than other methods in competition for audio source separation in blind scenario.

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Index Terms

Computer Science
Information Sciences

Keywords

Blind Source Separation Complete Decomposition Clustering K-mean Clustering