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SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES

Abstract: 

In this article, we propose a Bounded Component Analysis (BCA) approach for the separation of the convolutive mixtures of sparse sources. The corresponding algorithm is derived from a geometric objective function defined over a completely deterministic setting. Therefore, it is applicable to sources which can be independent or dependent in both space and time dimensions. We show that all global optima of the proposed objective are perfect separators. We also provide numerical examples to illustrate the performance of the algorithm.

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Paper Details

Authors:
Eren Babatas
Submitted On:
12 April 2018 - 12:32pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
ALPER TUNGA ERDOGAN
Paper Code:
ICASSP18001
Document Year:
2018
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Document Files

posterconvolutivesparsebca.pdf

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[1] Eren Babatas, "SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2433. Accessed: Oct. 18, 2019.
@article{2433-18,
url = {http://sigport.org/2433},
author = {Eren Babatas },
publisher = {IEEE SigPort},
title = {SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES},
year = {2018} }
TY - EJOUR
T1 - SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES
AU - Eren Babatas
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2433
ER -
Eren Babatas. (2018). SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES. IEEE SigPort. http://sigport.org/2433
Eren Babatas, 2018. SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES. Available at: http://sigport.org/2433.
Eren Babatas. (2018). "SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES." Web.
1. Eren Babatas. SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2433