File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A new recursive algorithm for time-varying autoregressive (TVAR) model estimation and its application to speech analysis

TitleA new recursive algorithm for time-varying autoregressive (TVAR) model estimation and its application to speech analysis
Authors
KeywordsEstimation errors
Model estimation
Observation errors
Prior information
QR decomposition
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089
Citation
The 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 1026-1029 How to Cite?
AbstractThis paper proposes a new state-regularized (SR) and QR decomposition based recursive least squares (QRRLS) algorithm with variable forgetting factor (VFF) for recursive coefficient estimation of time-varying autoregressive (AR) models. It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance and bias over traditional regularized RLS algorithm. It also increases the tracking speed by introducing a new measure of convergence status to control the FF. Simulations using synthetic and real speech signals show that the proposed method has improved tracking performance and reduced estimation error variance than conventional TVAR modeling methods during rapid changing of AR coefficients. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/160248
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChu, Yen_US
dc.contributor.authorChan, SCen_US
dc.contributor.authorZhang, Zen_US
dc.contributor.authorTsui, KMen_US
dc.date.accessioned2012-08-16T06:06:36Z-
dc.date.available2012-08-16T06:06:36Z-
dc.date.issued2012en_US
dc.identifier.citationThe 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 1026-1029en_US
dc.identifier.isbn978-1-4673-0219-7-
dc.identifier.issn0271-4302-
dc.identifier.urihttp://hdl.handle.net/10722/160248-
dc.description.abstractThis paper proposes a new state-regularized (SR) and QR decomposition based recursive least squares (QRRLS) algorithm with variable forgetting factor (VFF) for recursive coefficient estimation of time-varying autoregressive (AR) models. It employs the estimated coefficients as prior information to minimize the exponentially weighted observation error, which leads to reduced variance and bias over traditional regularized RLS algorithm. It also increases the tracking speed by introducing a new measure of convergence status to control the FF. Simulations using synthetic and real speech signals show that the proposed method has improved tracking performance and reduced estimation error variance than conventional TVAR modeling methods during rapid changing of AR coefficients. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000089-
dc.relation.ispartofIEEE International Symposium on Circuits and Systems Proceedingsen_US
dc.rightsIEEE International Symposium on Circuits and Systems Proceedings. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectEstimation errors-
dc.subjectModel estimation-
dc.subjectObservation errors-
dc.subjectPrior information-
dc.subjectQR decomposition-
dc.titleA new recursive algorithm for time-varying autoregressive (TVAR) model estimation and its application to speech analysisen_US
dc.typeConference_Paperen_US
dc.identifier.emailChu, Y: yjchu@eee.hku.hken_US
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_US
dc.identifier.emailZhang, Z: zhangzg@hku.hken_US
dc.identifier.emailTsui, KM: kmtsui11@hku.hk-
dc.identifier.authorityChan, SC=rp00094en_US
dc.identifier.authorityZhang, Z=rp01565en_US
dc.identifier.authorityTsui, KM=rp00181en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISCAS.2012.6271402-
dc.identifier.scopuseid_2-s2.0-84866617726-
dc.identifier.hkuros203543en_US
dc.identifier.spage1026-
dc.identifier.epage1029-
dc.publisher.placeUnited States-
dc.description.otherThe 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea, 20-23 May 2012. In IEEE International Symposium on Circuits and Systems Proceedings, 2012, p. 1026-1029-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats