Traditional music heritage in college piano teaching combined with time series modelling

Abstract

This paper uses the ARIMA model in time series and the BLSTM sentiment classification algorithm in sentiment analysis to predict the elements and the direction of traditional music heritage. Differential processing of non-smooth series data stabilizes the time series data of traditional music inheritance. By extracting all the features contained in the sheet music, the interpreted sheet music is subjected to sentiment analysis to further analyze the inheritable elements of traditional music. The results show that the time series model has high accuracy in predicting the inheritable elements of traditional music, the MAPE value of the ARIMA model is 5.9658308, and the melody, as well as the structure of traditional music, can be integrated into piano teaching in colleges and universities to a certain extent, with the integration degree of the melody being 0.72 and the integration degree of the structure is 0.655.

Keywords

• arima model
• bert model
• sentiment classification algorithm
• college piano teaching
• traditional music
• 00a65