A novel voice signal discrimination algorithm and its application
A new algorithm which is capable of classifying voice signals recorded from subjects before and after the anesthetic procedure is presented. This new algorithm is based on Wavelet Packet Analysis (WPA) and Least Squares Support Vector Machines (LSSVM) and it combines the coefficients of WPA with oth...
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| creator | Yu Wei Han Qiang Hosseini, H. G. Cameron, A. |
| description | A new algorithm which is capable of classifying voice signals recorded from subjects before and after the anesthetic procedure is presented. This new algorithm is based on Wavelet Packet Analysis (WPA) and Least Squares Support Vector Machines (LSSVM) and it combines the coefficients of WPA with other parameters, such as Spectral Centroid, Spectral roll-off point etc., as the feature vector. Experimental evaluation has shown that the proposed classification algorithm based on WPA and LSSVM is very effective as compared to the other two methods, and the total accuracy rate is over 85%. |
| doi_str_mv | 10.1109/MSNA.2012.6324540 |
| format | Conference Proceeding |
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Experimental evaluation has shown that the proposed classification algorithm based on WPA and LSSVM is very effective as compared to the other two methods, and the total accuracy rate is over 85%.</description><identifier>ISBN: 9781467324656</identifier><identifier>ISBN: 1467324655</identifier><identifier>EISBN: 9781467324670</identifier><identifier>EISBN: 1467324663</identifier><identifier>EISBN: 1467324671</identifier><identifier>EISBN: 9781467324663</identifier><identifier>DOI: 10.1109/MSNA.2012.6324540</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; Classification algorithms ; Feature extraction ; Feature Selection ; Least Squares Support Vector Machines ; Speech ; Support vector machines ; Wavelet Packet Analysis ; Wavelet packets</subject><ispartof>2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 2012, Vol.1, p.169-172</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6324540$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,778,782,787,788,2054,27908,54903</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6324540$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yu Wei</creatorcontrib><creatorcontrib>Han Qiang</creatorcontrib><creatorcontrib>Hosseini, H. 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Experimental evaluation has shown that the proposed classification algorithm based on WPA and LSSVM is very effective as compared to the other two methods, and the total accuracy rate is over 85%.</description><subject>Accuracy</subject><subject>Classification algorithms</subject><subject>Feature extraction</subject><subject>Feature Selection</subject><subject>Least Squares Support Vector Machines</subject><subject>Speech</subject><subject>Support vector machines</subject><subject>Wavelet Packet Analysis</subject><subject>Wavelet packets</subject><isbn>9781467324656</isbn><isbn>1467324655</isbn><isbn>9781467324670</isbn><isbn>1467324663</isbn><isbn>1467324671</isbn><isbn>9781467324663</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVj01LxDAYhCMiKGt_gHjJH2jN23wfy-IXrHpw70uaJusr2bY0ZcF_b9G97FyGh4FhhpA7YBUAsw9vn-9NVTOoK8VrIQW7IIXVBoTSCyvNLs9YqmtS5PzNFhkwAsQNaRraD8eQ6HFAH2jGfe8S7TD7CQ_YuxmHnrq0Hyacvw7U9R3FOVM3jgn9X3pLrqJLORQnX5Ht0-N2_VJuPp5f182mRMvmkptguFE-CpBa6mBFDFxZC1YIAC5aKboWpAUfQ9RgfYi2tZ2sdRvZgnxF7v9rMYSwG5d1bvrZnX7zX3ziSwo</recordid><startdate>201208</startdate><enddate>201208</enddate><creator>Yu Wei</creator><creator>Han Qiang</creator><creator>Hosseini, H. 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G. ; Cameron, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-38e8386cf415757e94fe369919441134b54db1591cfef719cef9b9d527bf019c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>Classification algorithms</topic><topic>Feature extraction</topic><topic>Feature Selection</topic><topic>Least Squares Support Vector Machines</topic><topic>Speech</topic><topic>Support vector machines</topic><topic>Wavelet Packet Analysis</topic><topic>Wavelet packets</topic><toplevel>online_resources</toplevel><creatorcontrib>Yu Wei</creatorcontrib><creatorcontrib>Han Qiang</creatorcontrib><creatorcontrib>Hosseini, H. G.</creatorcontrib><creatorcontrib>Cameron, A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yu Wei</au><au>Han Qiang</au><au>Hosseini, H. G.</au><au>Cameron, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A novel voice signal discrimination algorithm and its application</atitle><btitle>2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA)</btitle><stitle>MSNA</stitle><date>2012-08</date><risdate>2012</risdate><volume>1</volume><spage>169</spage><epage>172</epage><pages>169-172</pages><isbn>9781467324656</isbn><isbn>1467324655</isbn><eisbn>9781467324670</eisbn><eisbn>1467324663</eisbn><eisbn>1467324671</eisbn><eisbn>9781467324663</eisbn><abstract>A new algorithm which is capable of classifying voice signals recorded from subjects before and after the anesthetic procedure is presented. This new algorithm is based on Wavelet Packet Analysis (WPA) and Least Squares Support Vector Machines (LSSVM) and it combines the coefficients of WPA with other parameters, such as Spectral Centroid, Spectral roll-off point etc., as the feature vector. 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| identifier | ISBN: 9781467324656 |
| ispartof | 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 2012, Vol.1, p.169-172 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Classification algorithms Feature extraction Feature Selection Least Squares Support Vector Machines Speech Support vector machines Wavelet Packet Analysis Wavelet packets |
| title | A novel voice signal discrimination algorithm and its application |
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