Intelligent generation and optimization of resources in music teaching reform based on artificial intelligence and deep learning Article Swipe
Cheng Ding
,
Xiaoyu Qu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-16458-8
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-16458-8
In order to increase the effectiveness and personalization of music instruction, this paper aims to create a deep reinforcement learning (DRL)-based framework for creating music resources. Therefore, a Melody Generation Model in Music Education Based on Actor-Critic Framework (AC-MGME) is proposed. This model analyzes students' learning status in real time through AC-MGME algorithm, generates melodies that match their abilities, and enhances the polyphonic generation effect by using multi-label classification and attention mechanism. According to the testing results, the proposed model clearly outperforms the baseline Deep Q-Network (DQN) algorithm, achieving 95.95% accuracy and 91.02% F1 score in melody generation quality with a generation time of 2.69 s. Therefore, the constructed model can not only generate high-quality personalized melody, but also shows a significant improvement in improving user experience and learning effect, providing reference direction for the generation and optimization of intelligent resources in music teaching.
Related Topics
Concepts
Computer science
Polyphony
Personalization
Melody
Reinforcement learning
Artificial intelligence
Deep learning
Personalized learning
Baseline (sea)
Quality (philosophy)
Machine learning
Multimedia
Teaching method
Cooperative learning
Mathematics education
World Wide Web
Musical
Physics
Mathematics
Oceanography
Acoustics
Philosophy
Open learning
Visual arts
Art
Epistemology
Geology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-16458-8
- https://www.nature.com/articles/s41598-025-16458-8.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413309572
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413309572Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-16458-8Digital Object Identifier
- Title
-
Intelligent generation and optimization of resources in music teaching reform based on artificial intelligence and deep learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-17Full publication date if available
- Authors
-
Cheng Ding, Xiaoyu QuList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-16458-8Publisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-025-16458-8.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41598-025-16458-8.pdfDirect OA link when available
- Concepts
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Computer science, Polyphony, Personalization, Melody, Reinforcement learning, Artificial intelligence, Deep learning, Personalized learning, Baseline (sea), Quality (philosophy), Machine learning, Multimedia, Teaching method, Cooperative learning, Mathematics education, World Wide Web, Musical, Physics, Mathematics, Oceanography, Acoustics, Philosophy, Open learning, Visual arts, Art, Epistemology, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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