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SPARSE BOUNDED COMPONENT ANALYSIS FOR CONVOLUTIVE MIXTURES

Citation Author(s):
Eren Babatas
Submitted by:
Alper Erdogan
Last updated:
12 April 2018 - 12:32pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
ALPER TUNGA ERDOGAN
Paper Code:
ICASSP18001

Abstract 

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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