A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control
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 me...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | EURASIP Journal on Audio, Speech, and Music Processing |
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| Online Access: | https://doi.org/10.1186/s13636-025-00391-9 |
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| author | Dong Li Zhenfang Liu |
| author_facet | Dong Li Zhenfang Liu |
| author_sort | Dong Li |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-7871cb2cabf549d2862c5b053b621848 |
| institution | Kabale University |
| issn | 1687-4722 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Audio, Speech, and Music Processing |
| spelling | doaj-art-7871cb2cabf549d2862c5b053b6218482025-01-26T12:46:08ZengSpringerOpenEURASIP Journal on Audio, Speech, and Music Processing1687-47222025-01-012025111810.1186/s13636-025-00391-9A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic controlDong Li0Zhenfang Liu1Conservatory of Music, Nanjing Normal UniversitySchool of Art and Design, Hubei University of TechnologyAbstract 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%.https://doi.org/10.1186/s13636-025-00391-9Deep neuro-fuzzy controlMusic learningPerformance assessmentFuzzificationAdaptability |
| spellingShingle | Dong Li Zhenfang Liu A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control EURASIP Journal on Audio, Speech, and Music Processing Deep neuro-fuzzy control Music learning Performance assessment Fuzzification Adaptability |
| title | A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control |
| title_full | A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control |
| title_fullStr | A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control |
| title_full_unstemmed | A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control |
| title_short | A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control |
| title_sort | big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control |
| topic | Deep neuro-fuzzy control Music learning Performance assessment Fuzzification Adaptability |
| url | https://doi.org/10.1186/s13636-025-00391-9 |
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