A bezier curve approximation of the speech signal in the classification process of laryngopathies
The research concerns a computer-based clinical decision support for laryngopathies. The classification process is based on a speech signal analysis in the time domain using recurrent neural networks. In our experiments, we use the modified Elman-Jordan neural network. In the preprocessing step, an...
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| creator | Szkola, J. Pancerz, K. Warchol, J. |
| description | The research concerns a computer-based clinical decision support for laryngopathies. The classification process is based on a speech signal analysis in the time domain using recurrent neural networks. In our experiments, we use the modified Elman-Jordan neural network. In the preprocessing step, an original signal is approximated using Bezier curves and next the neural network is trained. Bezier curve approximation reduces the amount of data to be learned as well as removes a noise from the original signal. |
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The classification process is based on a speech signal analysis in the time domain using recurrent neural networks. In our experiments, we use the modified Elman-Jordan neural network. In the preprocessing step, an original signal is approximated using Bezier curves and next the neural network is trained. Bezier curve approximation reduces the amount of data to be learned as well as removes a noise from the original signal.</description><subject>approximation</subject><subject>Approximation algorithms</subject><subject>Approximation methods</subject><subject>Bezier curves</subject><subject>computer-based clinical decision support</subject><subject>Diseases</subject><subject>laryngopathies</subject><subject>Larynx</subject><subject>Recurrent neural networks</subject><subject>Speech</subject><subject>Vectors</subject><isbn>9781457700415</isbn><isbn>1457700417</isbn><isbn>9788360810354</isbn><isbn>8360810397</isbn><isbn>8360810354</isbn><isbn>9788360810392</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotT81qwzAY8xiDjS5PsItfIOB_O8dS9geFXXovn53PjUeWBDsba5--WTtdhIQk0A2pGuucNMxxJrW6vWiutLWMKa7vSVXKJ1tgjGukfCCwph5PCTMN3_kHKUxTHn_TF8xpHOgY6dwhLRNi6GhJhwF6moaLGXooJcUUrtGlFrCUv0oP-TgcxgnmLmF5JHcR-oLVP6_I7uV5t3mrtx-v75v1tk4Nm-tWypaLyB2LyoMxXgQMrY0BIjZGoWstxIhglXEShHbAtYggeAOt91rKFXm6ziZE3E95uZCPe8OsE9rKM8_4U5I</recordid><startdate>201109</startdate><enddate>201109</enddate><creator>Szkola, J.</creator><creator>Pancerz, K.</creator><creator>Warchol, J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201109</creationdate><title>A bezier curve approximation of the speech signal in the classification process of laryngopathies</title><author>Szkola, J. ; Pancerz, K. ; Warchol, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-d33d12f180f4ba66b2cecd7fcafe964e8d7affea74683a258a152fa219adbb533</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>approximation</topic><topic>Approximation algorithms</topic><topic>Approximation methods</topic><topic>Bezier curves</topic><topic>computer-based clinical decision support</topic><topic>Diseases</topic><topic>laryngopathies</topic><topic>Larynx</topic><topic>Recurrent neural networks</topic><topic>Speech</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Szkola, J.</creatorcontrib><creatorcontrib>Pancerz, K.</creatorcontrib><creatorcontrib>Warchol, J.</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>Szkola, J.</au><au>Pancerz, K.</au><au>Warchol, J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A bezier curve approximation of the speech signal in the classification process of laryngopathies</atitle><btitle>2011 Federated Conference on Computer Science and Information Systems (FedCSIS)</btitle><stitle>FedCSIS</stitle><date>2011-09</date><risdate>2011</risdate><spage>141</spage><epage>146</epage><pages>141-146</pages><isbn>9781457700415</isbn><isbn>1457700417</isbn><eisbn>9788360810354</eisbn><eisbn>8360810397</eisbn><eisbn>8360810354</eisbn><eisbn>9788360810392</eisbn><abstract>The research concerns a computer-based clinical decision support for laryngopathies. The classification process is based on a speech signal analysis in the time domain using recurrent neural networks. In our experiments, we use the modified Elman-Jordan neural network. In the preprocessing step, an original signal is approximated using Bezier curves and next the neural network is trained. Bezier curve approximation reduces the amount of data to be learned as well as removes a noise from the original signal.</abstract><pub>IEEE</pub><tpages>6</tpages></addata></record> |
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| subjects | approximation Approximation algorithms Approximation methods Bezier curves computer-based clinical decision support Diseases laryngopathies Larynx Recurrent neural networks Speech Vectors |
| title | A bezier curve approximation of the speech signal in the classification process of laryngopathies |
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