<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, according to the characteristics and development trend of traditional opera, based on the deep learning model, we use a convolutional neural...

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

Data publikacji: 20 lis 2023

Zakres stron: -

Otrzymano: 17 sty 2023

Przyjęty: 31 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01182

Słowa kluczowe
CNN-SVM classification model, Deep learning model, Timbre features, Traditional opera, Ethnic vocal music

© 2023 Juncai Hou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 20 lis 2023

Zakres stron: -

Otrzymano: 17 sty 2023

Przyjęty: 31 maj 2023

DOI: https://doi.org/10.2478/amns.2023.2.01182

Słowa kluczoweCNN-SVM classification model, Deep learning model, Timbre features, Traditional opera, Ethnic vocal music

© 2023 Juncai Hou, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
CNN-SVM classification model, Deep learning model, Timbre features, Traditional opera, Ethnic vocal music