Zobrazit v EDS

Intelligent generation and optimization of resources in music teaching reform based on artificial intelligence and deep learning.

Uloženo v:
Podrobná bibliografie
Název: Intelligent generation and optimization of resources in music teaching reform based on artificial intelligence and deep learning.
Autoři: Cheng, Ding, Qu, Xiaoyu
Zdroj: Scientific Reports; 8/17/2025, Vol. 15 Issue 1, p1-17, 17p
Témata: ARTIFICIAL intelligence, DEEP learning, MELODIC analysis, USER experience, MUSIC education, INDIVIDUALIZED instruction, REINFORCEMENT learning
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Copyright of Scientific Reports 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.)
Databáze: Complementary Index
Popis
Abstrakt: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. [ABSTRACT FROM AUTHOR]
ISSN:20452322
DOI:10.1038/s41598-025-16458-8