MULTI-SOURCE AND HETEROGENEOUS ONLINE MUSIC EDUCATION MECHANISM: AN ARTIFICIAL INTELLIGENCE-DRIVEN APPROACH

Yuanyuan Yang, Raveena Judie Dolly (), Madini O. Alassafi (), Adam Slowik () and Fawaz E. Alsaadi ()
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Yuanyuan Yang: Xinxiang University, Xinxiang 453003, P. R. China
Raveena Judie Dolly: ��Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India
Madini O. Alassafi: ��Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Adam Slowik: �Koszalin University of Technology, Koszalin, Poland
Fawaz E. Alsaadi: ��Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-10

Abstract: In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, which can provide personalized learning course resource recommendations for music online learners. The system includes four layers, consisting of user interface layer, application module layer, function module layer and data storage layer. At the application module level, this paper proposes a music recommendation algorithm based on a personalized multimodal network model. The recommendation algorithm performs music information retrieval (MIR) based on the similarity judgment of the contour of music pitch and the overall change, and constructs a multimodal network model based on the user’s preference for resources to achieve personalized music recommendation. This paper crawls more than one million music score data from a well-known music platform database in China to establish a dataset to evaluate the performance of this method. The comparison results with three existing works show that the method proposed in this paper has good performance and can provide users with suitable music recommendations. The artificial intelligence technology-driven online music education mechanism proposed in this paper has good prospects.

Keywords: Artificial Intelligence; Heterogeneous Online Music Education; Personalized Recommendation; Multimodal Network Model; Clustering Algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X23401540

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