<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the development of music education and artificial intelligence technology, it is increasingly common to assist teaching with the help of artificial intelligence technology in music education at present. This paper analyzes the role of intelligent transformation in music teaching, discusses its necessity, and proposes the direction of constructing an intelligent music teaching system accordingly. In the construction of the intelligent music teaching system, the short-time autocorrelation function method, the average amplitude difference function method, the inverted spectrum method, and the median smoothing and linear smoothing algorithms in the fundamental tone smoothing algorithm are introduced in the audio feature extraction algorithm. With the RBF algorithm as the core of the system design, the layers of the neural network structure are sequentially attributed to the implicit layer, the input layer, and the output layer to construct the RBF model for students to learn music knowledge. Taking first-year students majoring in music at Z University in Jiangsu province as research subjects, the use of an intelligent music teaching system is being carried out in teaching practice. The students’ sound sense and music sense improved by 15.33 and 14.25 compared to the pre-practice period, showing significant differences (P&lt;0.05). A highly significant difference (P&lt;0.01) was demonstrated in all dimensions of music literacy.</p>
</abstract>

With the development of music education and artificial intelligence technology, it is increasingly common to assist teaching with the help of artificial intelligence technology in music education at present. This paper analyzes the role of intelligent transformation in music teaching, discusses its necessity, and proposes the direction of constructing an intelligent music teaching system accordingly. In the construction of the intelligent music teaching system, the short-time autocorrelation function method, the average amplitude difference function method, the inverted spectrum method, and the median smoothing and linear smoothing algorithms in the fundamental tone smoothing algorithm are introduced in the audio feature extraction algorithm. With the RBF algorithm as the core of the system design, the layers of the neural network structure are sequentially attributed to the implicit layer, the input layer, and the output layer to construct the RBF model for students to learn music knowledge. Taking first-year students majoring in music at Z University in Jiangsu province as research subjects, the use of an intelligent music teaching system is being carried out in teaching practice. The students’ sound sense and music sense improved by 15.33 and 14.25 compared to the pre-practice period, showing significant differences (P<0.05). A highly significant difference (P<0.01) was demonstrated in all dimensions of music literacy.

Artificial Intelligence Technology Drives Intelligent Transformation of Music Education

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the development of music education and artificial intelligence technology, it is increasingly common to assist teaching with the help of artificial...

Artificial Intelligence Technology Drives Intelligent Transformation of Music Education

Publié en ligne: 05 août 2024

Pages: -

Reçu: 14 avr. 2024

Accepté: 01 juil. 2024

DOI: https://doi.org/10.2478/amns-2024-1947

Mots clés
Artificial intelligence technology, Audio feature extraction, RBF algorithm, Fundamental smoothing, Music education

© 2024 Chunbo Hou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artificial Intelligence Technology Drives Intelligent Transformation of Music Education

Publié en ligne: 05 août 2024

Pages: -

Reçu: 14 avr. 2024

Accepté: 01 juil. 2024

DOI: https://doi.org/10.2478/amns-2024-1947

Mots clésArtificial intelligence technology, Audio feature extraction, RBF algorithm, Fundamental smoothing, Music education

© 2024 Chunbo Hou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Artificial intelligence technology, Audio feature extraction, RBF algorithm, Fundamental smoothing, Music education