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Jang, Gil-Jin
Machine Intelligence Lab
Research Interests
  • Acoustic signal processing
  • Computer vision
  • Biomedical signal processing

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Independent vector analysis based on overlapped cliques of variable width for frequency-domain blind signal separation

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dc.contributor.author Lee, Intae ko
dc.contributor.author Jang, Gil-Jin ko
dc.date.available 2014-04-09T08:23:39Z -
dc.date.created 2013-06-18 ko
dc.date.issued 2012-05 -
dc.identifier.citation EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, v.2012, no.1, pp. - ko
dc.identifier.issn 1687-6180 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/2555 -
dc.identifier.uri http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84872982333 ko
dc.description.abstract A novel method is proposed to improve the performance of independent vector analysis (IVA) for blind signal separation of acoustic mixtures. IVA is a frequency-domain approach that successfully resolves the well-known permutation problem by applying a spherical dependency model to all pairs of frequency bins. The dependency model of IVA is equivalent to a single clique in an undirected graph; a clique in graph theory is defined as a subset of vertices in which any pair of vertices is connected by an undirected edge. Therefore, IVA imposes the same amount of statistical dependency on every pair of frequency bins, which may not match the characteristics of real-world signals. The proposed method allows variable amounts of statistical dependencies according to the correlation coefficients observed in real acoustic signals and, hence, enables more accurate modeling of statistical dependencies. A number of cliques constitutes the new dependency graph so that neighboring frequency bins are assigned to the same clique, while distant bins are assigned to different cliques. The permutation ambiguity is resolved by overlapped frequency bins between neighboring cliques. For speech signals, we observed especially strong correlations across neighboring frequency bins and a decrease in these correlations with an increase in the distance between bins. The clique sizes are either fixed, or determined by the reciprocal of the mel-frequency scale to impose a wider dependency on low-frequency components. Experimental results showed improved performances over conventional IVA. The signal-to-interference ratio improved from 15.5 to 18.8 dB on average for seven different source locations. When we varied the clique sizes according to the observed correlations, the stability of the proposed method increased with a large number of cliques. ko
dc.description.statementofresponsibility open -
dc.language ENG ko
dc.publisher SPRINGER INTERNATIONAL PUBLISHING AG ko
dc.subject Accurate modeling ko
dc.subject Acoustic mixtures ko
dc.subject Acoustic signals ko
dc.subject Blind Signal Separation ko
dc.subject Clique size ko
dc.subject Correlation coefficient ko
dc.subject Dependency graphs ko
dc.subject Dependency model ko
dc.subject Frequency bins ko
dc.subject Frequency domains ko
dc.subject Frequency-domain approach ko
dc.subject Independent vector analysis ko
dc.subject Low-frequency components ko
dc.subject Permutation ambiguity ko
dc.subject Permutation problems ko
dc.subject Signal to interference ratio ko
dc.subject Source location ko
dc.subject Speech signals ko
dc.subject Statistical dependencies ko
dc.subject Strong correlation ko
dc.subject Undirected graph ko
dc.subject Variable width ko
dc.title Independent vector analysis based on overlapped cliques of variable width for frequency-domain blind signal separation ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-84872982333 ko
dc.identifier.wosid 000306430400001 ko
dc.type.rims ART ko
dc.description.wostc 4 *
dc.description.scopustc 3 *
dc.date.tcdate 2015-02-28 *
dc.date.scptcdate 2014-08-19 *
dc.identifier.doi 10.1186/1687-6180-2012-113 ko
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