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Moving average estimator least mean square using echo cancellation algorithm

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dc.contributor.authorOh, Sang-Yeob-
dc.contributor.authorAhn, Chan-Shik-
dc.date.available2020-02-29T00:47:22Z-
dc.date.created2020-02-12-
dc.date.issued2013-01-
dc.identifier.issn1876-1100-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/14893-
dc.description.abstractEco cancellation algorithm should not only promptly adapt itself to changing environment but also minimize effects of a speech signal. However, since the color noise does not feature a consistent signal, it certainly has a significant influence on the speech signal. In this paper, the echo cancellation algorithm with a moving average LMS filter applied has been proposed. For the color noise cancellation method, an average estimator was measured by LMS adaptation filter techniques while a LMS filter step size was controlled. In addition, as it was designed to converge on a non-noise signal, the echo signal was cancelled which would, in return, lead it to the improvement of a performance. For the color noise environment, the echo cancellation Algorithm with the Average Estimator LMS filter used was applied and, a result to prove a convergence performance and stability to be improved by 10 dB comparing to the current method was gained. © 2013 Springer Science+Business Media.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer, Dordrecht-
dc.relation.isPartOfLecture Notes in Electrical Engineering-
dc.titleMoving average estimator least mean square using echo cancellation algorithm-
dc.typeArticle-
dc.type.rimsART-
dc.description.journalClass1-
dc.identifier.doi10.1007/978-94-007-5860-5_38-
dc.identifier.bibliographicCitationLecture Notes in Electrical Engineering, v.215 LNEE, pp.319 - 324-
dc.description.isOpenAccessN-
dc.identifier.scopusid2-s2.0-84874146554-
dc.citation.endPage324-
dc.citation.startPage319-
dc.citation.titleLecture Notes in Electrical Engineering-
dc.citation.volume215 LNEE-
dc.contributor.affiliatedAuthorOh, Sang-Yeob-
dc.type.docTypeConference Paper-
dc.subject.keywordAuthorAdaptive filter-
dc.subject.keywordAuthorEcho cancellation-
dc.subject.keywordAuthorLeast mean square (LMS) filter-
dc.subject.keywordAuthorMoving average estimator-
dc.subject.keywordAuthorNoise cancellation-
dc.subject.keywordPlusCancellation algorithms-
dc.subject.keywordPlusChanging environment-
dc.subject.keywordPlusColor noise-
dc.subject.keywordPlusConvergence performance-
dc.subject.keywordPlusEcho cancellation algorithm-
dc.subject.keywordPlusEcho signals-
dc.subject.keywordPlusFilter techniques-
dc.subject.keywordPlusLeast mean square (LMS)-
dc.subject.keywordPlusLeast mean squares-
dc.subject.keywordPlusMoving averages-
dc.subject.keywordPlusNoise cancellation-
dc.subject.keywordPlusSpeech signals-
dc.subject.keywordPlusStep size-
dc.subject.keywordPlusAdaptive filters-
dc.subject.keywordPlusAlgorithms-
dc.subject.keywordPlusEstimation-
dc.subject.keywordPlusNetwork security-
dc.subject.keywordPlusSignal denoising-
dc.subject.keywordPlusSpurious signal noise-
dc.subject.keywordPlusEcho suppression-
dc.description.journalRegisteredClassscopus-
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