The analysis of optimization in music aesthetic education under artificial intelligence

In the artificial intelligence (AI) domain, effectively integrating deep learning (DL) technology with the content, teaching methodologies, and learning processes of music aesthetic education remains a subject worthy of in-depth exploration and discussion. The aim is to meet to the music aesthetic n...

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Vydáno v:Scientific reports Ročník 15; číslo 1; s. 11545 - 13
Hlavní autor: Peng, Yixuan
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 04.04.2025
Nature Publishing Group
Nature Portfolio
Témata:
639/705
639/705/1041
639/705/1042
639/705/1045
639/705/1046
639/705/117
639/705/258
639/705/531
639/705/794
Accuracy
Adult
Algorithm optimization
Algorithms
Artificial Intelligence
Deep Learning
Education
Emotion recognition
Emotions
Esthetics
Female
Humanities and Social Sciences
Humans
Male
multidisciplinary
Music
Music - psychology
Music aesthetic education
Science
Science (multidisciplinary)
Young Adult
Deep learning
Emotion recognition
Artificial intelligence
Music aesthetic education
Algorithm optimization
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:In the artificial intelligence (AI) domain, effectively integrating deep learning (DL) technology with the content, teaching methodologies, and learning processes of music aesthetic education remains a subject worthy of in-depth exploration and discussion. The aim is to meet to the music aesthetic needs of students across different age groups and levels of musical literacy. In this paper, the concepts of AI and DL algorithm are first introduced, and their algorithm principles and application status are understood. Then, they are integrated into the application of music aesthetic education, and the algorithm principles and running codes are designed. Finally, experiments are carried out to verify the accuracy of music emotion recognition based on DL algorithm in AI environment to verify the effectiveness of music aesthetic education method based on DL. The results show that the algorithm proposed in this paper has higher accuracy, which combines the advantages of AI and DL algorithm, and obtains higher recognition accuracy. It provides more possibilities for future music aesthetic teaching activities. This paper is dedicated to investigating the feasibility and approach to optimizing the method of music aesthetic education through DL. Its objective is to chart a new developmental direction and practical pathway for music aesthetic education in the era of AI.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-96436-2