A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control.
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| Title: | A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control. |
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| Authors: | Li, Dong1 (AUTHOR) 10177@njnu.edu.cn, Liu, Zhenfang2 (AUTHOR) zhenfang_liu@outlook.com |
| Source: | EURASIP Journal on Audio Speech & Music Processing. 1/25/2025, Vol. 2025 Issue 1, p1-18. 18p. |
| Subjects: | Music classrooms, Information technology, Music education, Environmental music, Musical performance |
| Abstract: | Background: Music training for learners has improved greatly in recent years with the inclusion of information technology and optimization methods. The improvements focus on assisted learning, instruction suggestions, and performance assessments. Purpose: An adaptive instructive suggestion method (AISM) using deep neural fuzzy control (FC) is introduced in this paper to provide persistent assistance for technology-based music classrooms. This proposed method reduces learning errors by pursuing instructions based on the learner's level. The instructions are adaptable depending on the error and level independent of different suggestions. The suggestions are replicated for similar issues across various music learning classrooms, retaining the constant fuzzification. Materials and methods: The fuzzy control deviates at every new level, and errors are identified over the deviations from the instructions pursued. This control process verifies the input based on instruction deviations to prevent error repetitions. Therefore, the fuzzification relies on error normalization using common adaptive suggestions for different learning sessions. If the fuzzy control fails to match the existing instruction pursued, then new instructions are augmented to reduce errors that serve as the FC constraint. This constraint is pursued by unresolved previous errors to improve learning efficacy. Results: Thus, compared to other methods, the system improves adaptability by 13.9%, efficiency analysis by 9.02%, and constraint detection by 10.26%. [ABSTRACT FROM AUTHOR] |
| Copyright of EURASIP Journal on Audio Speech & Music Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 182467188 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Dong%22">Li, Dong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 10177@njnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhenfang%22">Liu, Zhenfang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhenfang_liu@outlook.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Audio+Speech+%26+Music+Processing%22">EURASIP Journal on Audio Speech & Music Processing</searchLink>. 1/25/2025, Vol. 2025 Issue 1, p1-18. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Music+classrooms%22">Music classrooms</searchLink><br /><searchLink fieldCode="DE" term="%22Information+technology%22">Information technology</searchLink><br /><searchLink fieldCode="DE" term="%22Music+education%22">Music education</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+music%22">Environmental music</searchLink><br /><searchLink fieldCode="DE" term="%22Musical+performance%22">Musical performance</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Music training for learners has improved greatly in recent years with the inclusion of information technology and optimization methods. The improvements focus on assisted learning, instruction suggestions, and performance assessments. Purpose: An adaptive instructive suggestion method (AISM) using deep neural fuzzy control (FC) is introduced in this paper to provide persistent assistance for technology-based music classrooms. This proposed method reduces learning errors by pursuing instructions based on the learner's level. The instructions are adaptable depending on the error and level independent of different suggestions. The suggestions are replicated for similar issues across various music learning classrooms, retaining the constant fuzzification. Materials and methods: The fuzzy control deviates at every new level, and errors are identified over the deviations from the instructions pursued. This control process verifies the input based on instruction deviations to prevent error repetitions. Therefore, the fuzzification relies on error normalization using common adaptive suggestions for different learning sessions. If the fuzzy control fails to match the existing instruction pursued, then new instructions are augmented to reduce errors that serve as the FC constraint. This constraint is pursued by unresolved previous errors to improve learning efficacy. Results: Thus, compared to other methods, the system improves adaptability by 13.9%, efficiency analysis by 9.02%, and constraint detection by 10.26%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of EURASIP Journal on Audio Speech & Music Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=182467188 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s13636-025-00391-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Music classrooms Type: general – SubjectFull: Information technology Type: general – SubjectFull: Music education Type: general – SubjectFull: Environmental music Type: general – SubjectFull: Musical performance Type: general Titles: – TitleFull: A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Dong – PersonEntity: Name: NameFull: Liu, Zhenfang IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 01 Text: 1/25/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 16874714 Numbering: – Type: volume Value: 2025 – Type: issue Value: 1 Titles: – TitleFull: EURASIP Journal on Audio Speech & Music Processing Type: main |
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