Practice of Music Therapy for Autistic Children Based on Music Data Mining
For children with autism, music therapy has aroused great concern with its novelty and better influence. Music therapy, as one of the effective treatment methods, has an important influence on the social interaction, behavior, and emotion of autistic children. This study attempts to explore a form o...
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Veröffentlicht in: | Mathematical problems in engineering 2022-04, Vol.2022, p.1-9 |
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description | For children with autism, music therapy has aroused great concern with its novelty and better influence. Music therapy, as one of the effective treatment methods, has an important influence on the social interaction, behavior, and emotion of autistic children. This study attempts to explore a form of applying highly specialized impromptu music therapy to the personal treatment of autistic children in schools for the disabled, as well as the design method of specific music activities. Based on music data mining, the machine learning method is introduced to model music emotion features, and various algorithms are compared to find a model with higher recognition rate, and, at the same time, the antinoise ability and generalization ability of the model are further improved. Finally, a music emotion cognitive model with better performance is established. The results show that the model can effectively promote the overall development of autistic children’s cognitive movement, social communication, language communication, and cognition. |
doi_str_mv | 10.1155/2022/4576211 |
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Music therapy, as one of the effective treatment methods, has an important influence on the social interaction, behavior, and emotion of autistic children. This study attempts to explore a form of applying highly specialized impromptu music therapy to the personal treatment of autistic children in schools for the disabled, as well as the design method of specific music activities. Based on music data mining, the machine learning method is introduced to model music emotion features, and various algorithms are compared to find a model with higher recognition rate, and, at the same time, the antinoise ability and generalization ability of the model are further improved. Finally, a music emotion cognitive model with better performance is established. The results show that the model can effectively promote the overall development of autistic children’s cognitive movement, social communication, language communication, and cognition.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2022/4576211</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Autism ; Autistic children ; Big Data ; Cognition ; Cognitive models ; Communication ; Data mining ; Discriminant analysis ; Emotions ; Language ; Machine learning ; Mathematical problems ; Music ; Music therapy ; Musical performances ; Musicians & conductors ; Patients ; Physiology ; Singers ; Social factors ; Therapy ; Wavelet transforms</subject><ispartof>Mathematical problems in engineering, 2022-04, Vol.2022, p.1-9</ispartof><rights>Copyright © 2022 Mingxun Wang et al.</rights><rights>Copyright © 2022 Mingxun Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1391-9275cbb5ebf4c54da4a29adf169b5933df4244db7b3304f66392cdea777fe5d83</cites><orcidid>0000-0002-7833-1913</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><contributor>Jan, Naeem</contributor><creatorcontrib>Wang, Mingxun</creatorcontrib><creatorcontrib>Luo, Gang</creatorcontrib><creatorcontrib>Chen, Hao</creatorcontrib><title>Practice of Music Therapy for Autistic Children Based on Music Data Mining</title><title>Mathematical problems in engineering</title><description>For children with autism, music therapy has aroused great concern with its novelty and better influence. Music therapy, as one of the effective treatment methods, has an important influence on the social interaction, behavior, and emotion of autistic children. This study attempts to explore a form of applying highly specialized impromptu music therapy to the personal treatment of autistic children in schools for the disabled, as well as the design method of specific music activities. Based on music data mining, the machine learning method is introduced to model music emotion features, and various algorithms are compared to find a model with higher recognition rate, and, at the same time, the antinoise ability and generalization ability of the model are further improved. Finally, a music emotion cognitive model with better performance is established. The results show that the model can effectively promote the overall development of autistic children’s cognitive movement, social communication, language communication, and cognition.</description><subject>Algorithms</subject><subject>Autism</subject><subject>Autistic children</subject><subject>Big Data</subject><subject>Cognition</subject><subject>Cognitive models</subject><subject>Communication</subject><subject>Data mining</subject><subject>Discriminant analysis</subject><subject>Emotions</subject><subject>Language</subject><subject>Machine learning</subject><subject>Mathematical problems</subject><subject>Music</subject><subject>Music therapy</subject><subject>Musical performances</subject><subject>Musicians & conductors</subject><subject>Patients</subject><subject>Physiology</subject><subject>Singers</subject><subject>Social factors</subject><subject>Therapy</subject><subject>Wavelet transforms</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNp90EtLAzEQB_AgCtbqzQ8Q8KhrM3lsusda37TooYK3kM3DTam7a7KL9Nu7pT17moH5McP8EboEcgsgxIQSSidcyJwCHKERiJxlArg8HnpCeQaUfZ6is5TWhFAQMB2h1_eoTReMw43Hyz4Fg1eVi7rdYt9EPOu7kIYxnldhY6Or8Z1OzuKmPuB73Wm8DHWov87Rideb5C4OdYw-Hh9W8-ds8fb0Mp8tMgOsgKygUpiyFK703AhuNde00NZDXpSiYMx6Tjm3pSwZI9znOSuosU5LKb0TdsrG6Gq_t43NT-9Sp9ZNH-vhpKL58C6hjMhB3eyViU1K0XnVxvCt41YBUbu01C4tdUhr4Nd7XoXa6t_wv_4D2AZn3w</recordid><startdate>20220406</startdate><enddate>20220406</enddate><creator>Wang, Mingxun</creator><creator>Luo, Gang</creator><creator>Chen, Hao</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-7833-1913</orcidid></search><sort><creationdate>20220406</creationdate><title>Practice of Music Therapy for Autistic Children Based on Music Data Mining</title><author>Wang, Mingxun ; Luo, Gang ; Chen, Hao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1391-9275cbb5ebf4c54da4a29adf169b5933df4244db7b3304f66392cdea777fe5d83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Autism</topic><topic>Autistic children</topic><topic>Big Data</topic><topic>Cognition</topic><topic>Cognitive models</topic><topic>Communication</topic><topic>Data mining</topic><topic>Discriminant analysis</topic><topic>Emotions</topic><topic>Language</topic><topic>Machine learning</topic><topic>Mathematical problems</topic><topic>Music</topic><topic>Music therapy</topic><topic>Musical performances</topic><topic>Musicians & conductors</topic><topic>Patients</topic><topic>Physiology</topic><topic>Singers</topic><topic>Social factors</topic><topic>Therapy</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Mingxun</creatorcontrib><creatorcontrib>Luo, Gang</creatorcontrib><creatorcontrib>Chen, Hao</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Mingxun</au><au>Luo, Gang</au><au>Chen, Hao</au><au>Jan, Naeem</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Practice of Music Therapy for Autistic Children Based on Music Data Mining</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2022-04-06</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>9</epage><pages>1-9</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>For children with autism, music therapy has aroused great concern with its novelty and better influence. Music therapy, as one of the effective treatment methods, has an important influence on the social interaction, behavior, and emotion of autistic children. This study attempts to explore a form of applying highly specialized impromptu music therapy to the personal treatment of autistic children in schools for the disabled, as well as the design method of specific music activities. Based on music data mining, the machine learning method is introduced to model music emotion features, and various algorithms are compared to find a model with higher recognition rate, and, at the same time, the antinoise ability and generalization ability of the model are further improved. Finally, a music emotion cognitive model with better performance is established. 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subjects | Algorithms Autism Autistic children Big Data Cognition Cognitive models Communication Data mining Discriminant analysis Emotions Language Machine learning Mathematical problems Music Music therapy Musical performances Musicians & conductors Patients Physiology Singers Social factors Therapy Wavelet transforms |
title | Practice of Music Therapy for Autistic Children Based on Music Data Mining |
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