The singing style of female roles in ethnic opera under artificial intelligence and deep neural networks

• Published in: Scientific reports Vol. 15; no. 1; pp. 20341 - 18
• Main Author: Yang, Huixia
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.06.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705; 639/705/1041; 639/705/1042; 639/705/1045; 639/705/1046; 639/705/117; 639/705/258; 639/705/531; 639/705/794; Accuracy; Algorithms; Artificial Intelligence; Attention mechanism; Bidirectional recurrent neural network; Classification; Deep Learning; Efficiency; Ethnic opera; Ethnicity; Female; Females; Genre; Humanities and Social Sciences; Humans; Information processing; Innovations; multidisciplinary; Music; Musical performances; Neural networks; Neural Networks, Computer; Residual gated convolution; Science; Science (multidisciplinary); Singing; Singing style classification; Time series; Attention mechanism; Bidirectional recurrent neural network; Ethnic opera; Singing style classification; Residual gated convolution
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-05429-8

With the rapid advancement of artificial intelligence technology, efficiently extracting and analyzing music performance style features has become an important topic in the field of music information processing. This work focuses on the classification of singing styles of female roles in ethnic opera and proposes an Attention-Enhanced 1D Residual Gated Convolutional and Bidirectional Recurrent Neural Network (ARGC-BRNN) model. The model uses a Residual Gated Linear Unit with Squeeze-and-Excitation (RGLU-SE) block to efficiently extract multi-level features of singing styles and combines a Bidirectional Recurrent Neural Network to model temporal dependencies. Finally, it uses an attention mechanism for global feature aggregation and classification. Experiments conducted on a self-constructed dataset of ethnic opera female role singing segments and the publicly available MagnaTagATune dataset show that the classification performance of the ARGC-BRNN model outperforms other comparison models. The model achieves an accuracy of 0.872 on the self-constructed dataset and an Area Under Curve of 0.912 on the MagnaTagATune dataset. The proposed model improves the results by 0.44% and 0.46%, respectively, compared to other models. The model also demonstrates significant advantages in training efficiency. The results indicate that the ARGC-BRNN model can effectively capture music singing style features, providing technical support for the digital and intelligent analysis of ethnic opera art.