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<p>In the age of information technology, the way of music teaching as a whole has changed, and music technology has brought great impetus to modern music education. The purpose of this paper is to research music teaching and musicians’ paper-and-pencil music-making habits. Firstly, based on the joint hidden semantic model and convolutional neural network, we make full use of the decisive advantage of a deep neural network to extract features automatically, obtain higher-level music feature representation from audio content, meanwhile abstract the historical behavioral information of users’ interaction with music into scoring data and integrate it into the recommender system to generate reasonable music recommendation for target users, and realize note recognition through convolutional neural network. After verifying and analyzing the effects of note recognition and music recommendation models, they are used in music teaching. The results show that the model’s recognition of musical notes is more satisfactory, and the music recommendation accuracy is improved by 6.45%~10% compared with the comparison algorithm, and it still has a specific predictive recommendation ability in a cold-start environment. The music performance of the experimental class that uses the model presented in this paper to assist in music teaching has improved by 5.08%. The note recognition and music recommendation model can be introduced into music teaching to promote the innovation and development of music teaching concepts and teaching methods and improve the effectiveness of music teaching.</p>
</abstract>

In the age of information technology, the way of music teaching as a whole has changed, and music technology has brought great impetus to modern music education. The purpose of this paper is to research music teaching and musicians’ paper-and-pencil music-making habits. Firstly, based on the joint hidden semantic model and convolutional neural network, we make full use of the decisive advantage of a deep neural network to extract features automatically, obtain higher-level music feature representation from audio content, meanwhile abstract the historical behavioral information of users’ interaction with music into scoring data and integrate it into the recommender system to generate reasonable music recommendation for target users, and realize note recognition through convolutional neural network. After verifying and analyzing the effects of note recognition and music recommendation models, they are used in music teaching. The results show that the model’s recognition of musical notes is more satisfactory, and the music recommendation accuracy is improved by 6.45%~10% compared with the comparison algorithm, and it still has a specific predictive recommendation ability in a cold-start environment. The music performance of the experimental class that uses the model presented in this paper to assist in music teaching has improved by 5.08%. The note recognition and music recommendation model can be introduced into music teaching to promote the innovation and development of music teaching concepts and teaching methods and improve the effectiveness of music teaching.

Research on the Application of Music Technology in Modern Music Education in the Information Age

Southwest University of Science and Technology

Applied Mathematics and Nonlinear Sciences

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

In the age of information technology, the way of music teaching as a whole has changed, and music technology has brought great impetus to modern music...

Research on the Application of Music Technology in Modern Music Education in the Information Age

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 18. Feb. 2024

Akzeptiert: 21. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1147

Schlüsselwörter
Hidden semantic model, Convolutional neural network, Note recognition, Music recommendation, Music education

© 2024 Yuanhui Wu, et al., published by Sciendo

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

Research on the Application of Music Technology in Modern Music Education in the Information Age

Online veröffentlicht: 22. Mai 2024

Seitenbereich: -

Eingereicht: 18. Feb. 2024

Akzeptiert: 21. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1147

SchlüsselwörterHidden semantic model, Convolutional neural network, Note recognition, Music recommendation, Music education

© 2024 Yuanhui Wu, et al., published by Sciendo

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

Schlüsselwörter
Hidden semantic model, Convolutional neural network, Note recognition, Music recommendation, Music education