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dc.contributor.authorSo, Stephenen_US
dc.contributor.authorPaliwal, Kuldipen_US
dc.date.accessioned2017-05-03T11:48:33Z
dc.date.available2017-05-03T11:48:33Z
dc.date.issued2011en_US
dc.date.modified2011-08-09T06:27:32Z
dc.identifier.issn01676393en_US
dc.identifier.doi10.1016/j.specom.2010.10.006en_AU
dc.identifier.urihttp://hdl.handle.net/10072/39786
dc.description.abstractIn this paper, we present a detailed analysis of the Kalman filter for the application of speech enhancement and identify its shortcomings when the linear predictor model parameters are estimated from speech that has been corrupted with additive noise. We show that when only noise-corrupted speech is available, the poor performance of the Kalman filter may be attributed to the presence of large values in the Kalman gain during low speech energy regions, which cause a large degree of residual noise to be present in the output. These large Kalman gain values result from poor estimates of the LPCs due to the presence of additive noise. This paper presents the analysis and application of the Kalman gain trajectory as a useful indicator of Kalman filter performance, which can be used to motivate further methods of improvement. As an example, we analyse the previously-reported application of long and overlapped tapered windows using Kalman gain trajectories to explain the reduction and smoothing of residual noise in the enhanced output. In addition, we investigate further extensions, such as Dolph-Chebychev windowing and iterative LPC estimation. This modified Kalman filter was found to have improved on the conventional and iterative versions of the Kalman filter in both objective and subjective testing.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherElsevier BVen_US
dc.publisher.placeNetherlandsen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom355en_US
dc.relation.ispartofpageto378en_US
dc.relation.ispartofissue3en_US
dc.relation.ispartofjournalSpeech Communicationen_US
dc.relation.ispartofvolume53en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchSignal Processingen_US
dc.subject.fieldofresearchcode090609en_US
dc.titleSuppressing the influence of additive noise on the Kalman gain for low residual noise speech enhancementen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2011
gro.hasfulltextNo Full Text


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