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Conference Paper: A new regularized TVAR-based algorithm for recursive detection of nonstationarity and its application to speech signals

TitleA new regularized TVAR-based algorithm for recursive detection of nonstationarity and its application to speech signals
Authors
KeywordsNonstationarity detection
Local likelihood
Wald test
TVAR
RLS
State regularization
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001269
Citation
The 2012 IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, MI., 5-8 August 2012. In IEEE/SP Workshop on Statistical Signal Processing Proceedings, 2012, p. 361-364 How to Cite?
AbstractThis paper develops a new recursive nonstationarity detection method based on time-varying autoregressive (TVAR) modeling. A local likelihood estimation approach is introduced which gives more weights to observations near the current time instant but less to those distance apart. It thus allows the Wald test to be computed based on RLS-type algorithms with low computational cost. A reliable and efficient state regularized variable forgetting factor (VFF) QR decomposition (QRD)-based RLS (SR-VFF-QRRLS) algorithm is adopted for estimation for its asymptotically unbiased property and immunity to lacking of excitation. Advantages of the proposed approach over conventional approaches are 1) it provides continuous parameter estimates and the corresponding stationary intervals with low complexity, 2) it mitigates low excitation problems using state regularization, and 3) stationarity at different scales can be detected by appropriately choosing a certain window size. The effectiveness of the proposed algorithm is evaluated by testing vocal tract changes in real speech signals. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/160247
ISBN

 

DC FieldValueLanguage
dc.contributor.authorChu, Yen_US
dc.contributor.authorChan, SCen_US
dc.contributor.authorZhang, Zen_US
dc.contributor.authorTsui, KMen_US
dc.date.accessioned2012-08-16T06:06:35Z-
dc.date.available2012-08-16T06:06:35Z-
dc.date.issued2012en_US
dc.identifier.citationThe 2012 IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, MI., 5-8 August 2012. In IEEE/SP Workshop on Statistical Signal Processing Proceedings, 2012, p. 361-364en_US
dc.identifier.isbn978-1-4673-0183-1-
dc.identifier.urihttp://hdl.handle.net/10722/160247-
dc.description.abstractThis paper develops a new recursive nonstationarity detection method based on time-varying autoregressive (TVAR) modeling. A local likelihood estimation approach is introduced which gives more weights to observations near the current time instant but less to those distance apart. It thus allows the Wald test to be computed based on RLS-type algorithms with low computational cost. A reliable and efficient state regularized variable forgetting factor (VFF) QR decomposition (QRD)-based RLS (SR-VFF-QRRLS) algorithm is adopted for estimation for its asymptotically unbiased property and immunity to lacking of excitation. Advantages of the proposed approach over conventional approaches are 1) it provides continuous parameter estimates and the corresponding stationary intervals with low complexity, 2) it mitigates low excitation problems using state regularization, and 3) stationarity at different scales can be detected by appropriately choosing a certain window size. The effectiveness of the proposed algorithm is evaluated by testing vocal tract changes in real speech signals. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001269-
dc.relation.ispartofIEEE/SP Workshop on Statistical Signal Processing Proceedingsen_US
dc.rightsIEEE/SP Workshop on Statistical Signal Processing 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.subjectNonstationarity detection-
dc.subjectLocal likelihood-
dc.subjectWald test-
dc.subjectTVAR-
dc.subjectRLS-
dc.subjectState regularization-
dc.titleA new regularized TVAR-based algorithm for recursive detection of nonstationarity and its application to speech signalsen_US
dc.typeConference_Paperen_US
dc.identifier.emailChu, Y: h0895503@hku.hken_US
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_US
dc.identifier.emailZhang, Z: zgzhang@eee.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/SSP.2012.6319704-
dc.identifier.scopuseid_2-s2.0-84868238035-
dc.identifier.hkuros203541en_US
dc.identifier.spage361-
dc.identifier.epage364-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 130408-

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