Abstract

Pop music multimedia is one of the popular digital pop music types. Based on the multiple intelligences teaching model, a multimedia multiple intelligences teaching method of popular music is proposed. This method not only analyzes the characteristics of pop music in detail, but also fully considers other important characteristics of pop music. It teaches college students multimedia, purifies their hearts, improves their personality, cultivates their innovative consciousness, and promotes their healthy growth. In this paper, the multiple intelligences teaching model is introduced into the process of pop music multimedia teaching path. In the stage of music audio segmentation, the deep belief network algorithm is used to accurately carry out music multimedia teaching. Finally, the experimental analysis results show that the integration of pop factors into music teaching, the combination of pop music and quality education, and the creation of music that is suitable for students’ personality characteristics and the needs of aesthetic development can better serve the needs of students’ quality improvement.

Highlights

  • With the continuous development of Internet technology and the improvement of technological progress, different Internet-based popular music multimedia began to emerge [1]

  • How to obtain the specific content source from the initial popular music multimedia data lacking the definition of popular music content has become a huge challenge for the current popular music multimedia multi-intelligence teaching, because the popular music multimedia signal belongs to a way of chronological order, the multiple intelligences teaching mode can be used according to its invisibility

  • E multiple intelligence teaching model is applied to the automatic classification process of popular music multimedia in this paper. is method can use the lyrics, word frequency, content, and meaning of popular music multimedia as current prior knowledge in the automatic classification process of popular music multimedia according to the features of automatic lyrics of popular music multimedia. e information gain method is used to obtain popular music characteristics of popular music multimedia content; this mode is used to limit the weight of lyrics and the semantic information of popular music, integrate lyrics with high meaning similarity, and construct various types of popular music multimedia teaching models

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Summary

Introduction

With the continuous development of Internet technology and the improvement of technological progress, different Internet-based popular music multimedia began to emerge [1]. Other classification methods are relatively simple, and it is not accurate to obtain the characteristics of popular music multimedia. E multiple intelligence teaching model is applied to the automatic classification process of popular music multimedia in this paper. Is method can use the lyrics, word frequency, content, and meaning of popular music multimedia as current prior knowledge in the automatic classification process of popular music multimedia according to the features of automatic lyrics of popular music multimedia. E information gain method is used to obtain popular music characteristics of popular music multimedia content; this mode is used to limit the weight of lyrics and the semantic information of popular music, integrate lyrics with high meaning similarity, and construct various types of popular music multimedia teaching models E multiple intelligence teaching model is applied to the automatic classification process of popular music multimedia in this paper. is method can use the lyrics, word frequency, content, and meaning of popular music multimedia as current prior knowledge in the automatic classification process of popular music multimedia according to the features of automatic lyrics of popular music multimedia. e information gain method is used to obtain popular music characteristics of popular music multimedia content; this mode is used to limit the weight of lyrics and the semantic information of popular music, integrate lyrics with high meaning similarity, and construct various types of popular music multimedia teaching models

Multiple Intelligence Teaching Mode
New Features under the Multiple Intelligence Teaching Mode
Findings
Conclusions
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