GMM-Based KLT-Domain Switched-Split Vector Quantization for LSF Coding

For quantization of line spectral frequency (LSF), Gaussian mixture model (GMM) based switched split vector quantization (SSVQ) has been reported as the best performing intra-frame coding method. However, GMM-SSVQ partly recovers correlations between the subvectors of split vector quantization (SVQ)...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE signal processing letters 2011-07, Vol.18 (7), p.415-418
Hauptverfasser:	Lee, Yoonjoo, Jung, Wonjin, Kim, Moo Young
Format:	Artikel
Sprache:	eng
Schlagworte:	Correlation Encoding GMM KLT LSF Shape Signal to noise ratio split vector quantization switched split vector quantization Transforms Vector quantization
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	418
container_issue	7
container_start_page	415
container_title	IEEE signal processing letters
container_volume	18
creator	Lee, Yoonjoo Jung, Wonjin Kim, Moo Young
description	For quantization of line spectral frequency (LSF), Gaussian mixture model (GMM) based switched split vector quantization (SSVQ) has been reported as the best performing intra-frame coding method. However, GMM-SSVQ partly recovers correlations between the subvectors of split vector quantization (SVQ). In the proposed GMM-SSVQ with the Karhunen-Loève Transform (KLT), KLT-domain quantization for each mixture with a novel region-clustering algorithm is applied to GMM-SSVQ. Compared with SVQ and GMM-SSVQ, it provides 4 and 1 bit higher performance in terms of average spectral distortion and outliers, respectively. Computational complexity and memory requirements are similar to GMM-SSVQ.
doi_str_mv	10.1109/LSP.2011.2154331
format	Article
fullrecord	<record><control><sourceid>crossref_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_LSP_2011_2154331</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5766714</ieee_id><sourcerecordid>10_1109_LSP_2011_2154331</sourcerecordid><originalsourceid>FETCH-LOGICAL-c193t-1af2dbbe2a1a164e3079f8d871e4bc361cc1704391e3288c2d2d529b3e6d46073</originalsourceid><addsrcrecordid>eNo9kMFKw0AQhhdRsFbvgpe8wNaZ3SS7e9Rqq5iikuo1bHYnutImJUkRfXpTWjz9w898w_AxdokwQQRzneUvEwGIE4FJLCUesREmieZCpng8zKCAGwP6lJ113RcAaNTJiM3miwW_tR356Clb8rtmbUMd5d-hd5_keb5ZhT56J9c3bfS6tXUffm0fmjqqhiLLZ9G08aH-OGcnlV11dHHIMXub3S-nDzx7nj9ObzLu0Mieo62EL0sSFi2mMUlQptJeK6S4dMOnzqGCWBokKbR2wgufCFNKSn2cgpJjBvu7rm26rqWq2LRhbdufAqHYeSgGD8XOQ3HwMCBXeyQQ0f96otJUYSz_ALNCV6U</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>GMM-Based KLT-Domain Switched-Split Vector Quantization for LSF Coding</title><source>IEEE Electronic Library (IEL)</source><creator>Lee, Yoonjoo ; Jung, Wonjin ; Kim, Moo Young</creator><creatorcontrib>Lee, Yoonjoo ; Jung, Wonjin ; Kim, Moo Young</creatorcontrib><description>For quantization of line spectral frequency (LSF), Gaussian mixture model (GMM) based switched split vector quantization (SSVQ) has been reported as the best performing intra-frame coding method. However, GMM-SSVQ partly recovers correlations between the subvectors of split vector quantization (SVQ). In the proposed GMM-SSVQ with the Karhunen-Loève Transform (KLT), KLT-domain quantization for each mixture with a novel region-clustering algorithm is applied to GMM-SSVQ. Compared with SVQ and GMM-SSVQ, it provides 4 and 1 bit higher performance in terms of average spectral distortion and outliers, respectively. Computational complexity and memory requirements are similar to GMM-SSVQ.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2011.2154331</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>IEEE</publisher><subject>Correlation ; Encoding ; GMM ; KLT ; LSF ; Shape ; Signal to noise ratio ; split vector quantization ; switched split vector quantization ; Transforms ; Vector quantization</subject><ispartof>IEEE signal processing letters, 2011-07, Vol.18 (7), p.415-418</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c193t-1af2dbbe2a1a164e3079f8d871e4bc361cc1704391e3288c2d2d529b3e6d46073</citedby><cites>FETCH-LOGICAL-c193t-1af2dbbe2a1a164e3079f8d871e4bc361cc1704391e3288c2d2d529b3e6d46073</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5766714$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5766714$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lee, Yoonjoo</creatorcontrib><creatorcontrib>Jung, Wonjin</creatorcontrib><creatorcontrib>Kim, Moo Young</creatorcontrib><title>GMM-Based KLT-Domain Switched-Split Vector Quantization for LSF Coding</title><title>IEEE signal processing letters</title><addtitle>LSP</addtitle><description>For quantization of line spectral frequency (LSF), Gaussian mixture model (GMM) based switched split vector quantization (SSVQ) has been reported as the best performing intra-frame coding method. However, GMM-SSVQ partly recovers correlations between the subvectors of split vector quantization (SVQ). In the proposed GMM-SSVQ with the Karhunen-Loève Transform (KLT), KLT-domain quantization for each mixture with a novel region-clustering algorithm is applied to GMM-SSVQ. Compared with SVQ and GMM-SSVQ, it provides 4 and 1 bit higher performance in terms of average spectral distortion and outliers, respectively. Computational complexity and memory requirements are similar to GMM-SSVQ.</description><subject>Correlation</subject><subject>Encoding</subject><subject>GMM</subject><subject>KLT</subject><subject>LSF</subject><subject>Shape</subject><subject>Signal to noise ratio</subject><subject>split vector quantization</subject><subject>switched split vector quantization</subject><subject>Transforms</subject><subject>Vector quantization</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMFKw0AQhhdRsFbvgpe8wNaZ3SS7e9Rqq5iikuo1bHYnutImJUkRfXpTWjz9w898w_AxdokwQQRzneUvEwGIE4FJLCUesREmieZCpng8zKCAGwP6lJ113RcAaNTJiM3miwW_tR356Clb8rtmbUMd5d-hd5_keb5ZhT56J9c3bfS6tXUffm0fmjqqhiLLZ9G08aH-OGcnlV11dHHIMXub3S-nDzx7nj9ObzLu0Mieo62EL0sSFi2mMUlQptJeK6S4dMOnzqGCWBokKbR2wgufCFNKSn2cgpJjBvu7rm26rqWq2LRhbdufAqHYeSgGD8XOQ3HwMCBXeyQQ0f96otJUYSz_ALNCV6U</recordid><startdate>201107</startdate><enddate>201107</enddate><creator>Lee, Yoonjoo</creator><creator>Jung, Wonjin</creator><creator>Kim, Moo Young</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>201107</creationdate><title>GMM-Based KLT-Domain Switched-Split Vector Quantization for LSF Coding</title><author>Lee, Yoonjoo ; Jung, Wonjin ; Kim, Moo Young</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c193t-1af2dbbe2a1a164e3079f8d871e4bc361cc1704391e3288c2d2d529b3e6d46073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Correlation</topic><topic>Encoding</topic><topic>GMM</topic><topic>KLT</topic><topic>LSF</topic><topic>Shape</topic><topic>Signal to noise ratio</topic><topic>split vector quantization</topic><topic>switched split vector quantization</topic><topic>Transforms</topic><topic>Vector quantization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Yoonjoo</creatorcontrib><creatorcontrib>Jung, Wonjin</creatorcontrib><creatorcontrib>Kim, Moo Young</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE signal processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lee, Yoonjoo</au><au>Jung, Wonjin</au><au>Kim, Moo Young</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>GMM-Based KLT-Domain Switched-Split Vector Quantization for LSF Coding</atitle><jtitle>IEEE signal processing letters</jtitle><stitle>LSP</stitle><date>2011-07</date><risdate>2011</risdate><volume>18</volume><issue>7</issue><spage>415</spage><epage>418</epage><pages>415-418</pages><issn>1070-9908</issn><eissn>1558-2361</eissn><coden>ISPLEM</coden><abstract>For quantization of line spectral frequency (LSF), Gaussian mixture model (GMM) based switched split vector quantization (SSVQ) has been reported as the best performing intra-frame coding method. However, GMM-SSVQ partly recovers correlations between the subvectors of split vector quantization (SVQ). In the proposed GMM-SSVQ with the Karhunen-Loève Transform (KLT), KLT-domain quantization for each mixture with a novel region-clustering algorithm is applied to GMM-SSVQ. Compared with SVQ and GMM-SSVQ, it provides 4 and 1 bit higher performance in terms of average spectral distortion and outliers, respectively. Computational complexity and memory requirements are similar to GMM-SSVQ.</abstract><pub>IEEE</pub><doi>10.1109/LSP.2011.2154331</doi><tpages>4</tpages></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISSN: 1070-9908
ispartof	IEEE signal processing letters, 2011-07, Vol.18 (7), p.415-418
issn	1070-9908 1558-2361
language	eng
recordid	cdi_crossref_primary_10_1109_LSP_2011_2154331
source	IEEE Electronic Library (IEL)
subjects	Correlation Encoding GMM KLT LSF Shape Signal to noise ratio split vector quantization switched split vector quantization Transforms Vector quantization
title	GMM-Based KLT-Domain Switched-Split Vector Quantization for LSF Coding
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T17%3A53%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=GMM-Based%20KLT-Domain%20Switched-Split%20Vector%20Quantization%20for%20LSF%20Coding&rft.jtitle=IEEE%20signal%20processing%20letters&rft.au=Lee,%20Yoonjoo&rft.date=2011-07&rft.volume=18&rft.issue=7&rft.spage=415&rft.epage=418&rft.pages=415-418&rft.issn=1070-9908&rft.eissn=1558-2361&rft.coden=ISPLEM&rft_id=info:doi/10.1109/LSP.2011.2154331&rft_dat=%3Ccrossref_RIE%3E10_1109_LSP_2011_2154331%3C/crossref_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5766714&rfr_iscdi=true