Abstract
In order to improve the effect of modern music teaching, this paper combines AI technology to construct a multimedia-assisted music teaching system, combines music teaching data processing requirements to improve the algorithm, proposes appropriate music data filtering algorithms, and performs appropriate data compression processing. Moreover, the functional structure analysis of the intelligent music teaching system is carried out with the support of the improved algorithm, and the three-tier framework technology that is currently more widely used is used in the music multimedia teaching system. Finally, in order to realize the complex functions of the system, the system adopts a layered approach. From the experimental research results, it can be seen that the multimedia-assisted music teaching system based on AI technology proposed in this paper can effectively improve the effect of modern music teaching.
Highlights
Ere is no difference between good and bad conditions, so that every student has equal educational opportunities, which helps to improve students’ music literacy and teaching quality [3]
Each student’s learning style is different and is not the same, and the traditional teaching mode cannot meet the learning needs of different students, and under the information mode, students can develop a learning plan that meets their personal characteristics according to their needs, so that they can fully explore intrinsic potential of the individual, improving students’ ability to learn independently. e music teaching system provides a wealth of music teaching resources, including multimedia courseware and teaching audiovisual
Is article applies AI technology to music teaching and combines multimedia technology to construct a multimedia-assisted music teaching system based on AI technology to improve the scientificity and effectiveness of music teaching
Summary
As per the regularity of wavelet basis, the vanishing moment order of wavelet function φ and the support of p have a greater influence on music compression; we will introduce the classification of wavelet basis and the relationship between wavelet basis and data compression. Since the energy of music is concentrated in low frequencies, the larger the vanishing moment of the filter, the less low-frequency energy leaks into the high-frequency subimages during wavelet decomposition, and the smaller the high-frequency coefficients located in the smooth area of the music. If the music is very smooth between the edge points, we must choose a wavelet with high-order vanishing moments to generate a large number of smallvalue wavelet coefficients. E symmetry of the tightly supported orthogonal wavelet function is equivalent to the linear phase characteristic of its filter.
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