The Influence of Traditional Opera Culture on the Development of Ethnic Vocal Music Art under Deep Learning Modeling

Abstract

In this paper, according to the characteristics and development trend of traditional opera, based on the deep learning model, we use a convolutional neural network to extract the opera features, combined with an SVM classifier to construct a CNN-SVM classification model. For the two classification algorithm models of logistic regression and deep confidence network, combined with two types of feature parameters of timbre and melody, six groups of experiments are designed to extract the time-frequency features of traditional opera. The CNN-SVM classification model is used to categorize the emotion of traditional opera, which aims to divide the musical features by multi-feature selection. Analyze the timbre feature parameter MFCC to investigate the impact of traditional opera timbre on ethnic vocal singing. For the logistic regression model, the coefficient of MFCCs is 0.5588, and the classification accuracy is only 0.5301 when the feature parameters are selected as melodic features, i.e., gene frequency, resonance peak, and band energy, and 0.6228 when the feature parameters are selected as a combination of timbral and melodic features. The diversity of the traditional opera timbres contributes to the development of ethnic vocal art with the trend of inclusiveness.

Keywords

• cnn-svm classification model
• deep learning model
• timbre features
• traditional opera
• ethnic vocal music
• 97m80