Noise Cancelling for Robust Speaker Identification Using Least Mean Square

Please use this identifier to cite or link to this item: http://repository.ipb.ac.id/handle/123456789/76445

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DC Field	Value	Language
dc.contributor.author	Permana, lnggih
dc.contributor.author	Buono, Agus
dc.contributor.author	Silalahi, Bib Paruhum
dc.date.accessioned	2015-10-07T03:40:52Z
dc.date.available	2015-10-07T03:40:52Z
dc.date.issued	2014
dc.identifier.issn	2356 542X	-
dc.identifier.uri	http://repository.ipb.ac.id/handle/123456789/76445
dc.description.abstract	This study focused on noise cancelling using Least Muns Square (Ll\IS) for robust speaker identifiration. Noise ranrrlling ran be used to oHrcome sptaker idrntification diffirulties in recognizing noisy voicrs. This study used the LMS on data preprocessing. Based on experimental results, it can be concluded that speaker identification using LMS at data preprocessing produces higher arcuracy than without using LMS. The highest accurary of data that using the LMS is 84.64•/e whereas the data without using the LMS only 2.11%.	en
dc.language.iso	id
dc.publisher	Faculty of Science and Technology, UIN Sultan Syarif Kasim Riau.
dc.relation.ispartofseries	Volume I. October 2014;	-
dc.subject.ddc	least means square	en
dc.subject.ddc	noise cancelling	en
dc.subject.ddc	speaktr identification	en
dc.title	Noise Cancelling for Robust Speaker Identification Using Least Mean Square	en
Appears in Collections:	Proceedings

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