Analysis of the Integration Teaching Mode of Traditional Music Elements and College Piano under the View of Big Data

Abstract

In this paper, the fusion teaching model was constructed, and the clustering of music elements was statistically calculated by calculating the intra-class distance of music elements. The feature vector of big data clustering is extracted, the traditional music element data is partitioned using linear FM signal, and the spatial matrix of traditional music element data is obtained after initializing the clustering center. To form the information flow model of big data time series, the phase space reconstruction analysis method is used to process piano data in nonlinear mapping. To achieve the objective function after clustering, adjust the weights within the fitness function, and then output the optimal program of the integrated teaching model. The results show that the post-test scores of students in the experimental group are higher than those of students in the control group, and the scores of tuning and composition have been improved by 2 and 2.5 points to reach the full score of 10 compared with those of the control group, which demonstrates the validity and feasibility of the fusion teaching mode of traditional music elements and college piano. © 2024 Dongming Hu et al., published by Sciendo.

Keywords

Author Keywords

Data clustering Feature vector Intraclass distance Phase space reconstruction Traditional music elements

Index Keywords

Students Big data Cluster analysis Clustering algorithms Teaching model Teaching modes Phase space methods Time series analysis Vector spaces Data clustering Features vector Intra class Intraclass distance Phase space reconstruction Phase spaces Space reconstruction Traditional music element

Author Affiliations

School of Music, Sichuan University of Science & Engineering, Zigong, Sichuan, China

Funding & Acknowledgements

No funding information

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Analysis of the Integration Teaching Mode of Traditional Music Elements and College Piano under the View of Big Data

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