RoleNet: A multiple features fusion network for role classification in cantonese opera

作     者：Li, Yue Peng, Zhengwei Xu, Di Chen, Yuanguang Chen, Guoan 

作者机构：School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou510006 China 

出 版 物：《Multimedia Tools and Applications》 (Multimedia Tools Appl)

年 卷 期：2025年

页      面：1-14页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Feature Selection 

摘      要：Cantonese opera, a key facet of Chinese traditional opera, boasts profound cultural and artistic value and has been designated as intangible cultural heritage. The use of certain roles is a basic concept in Cantonese opera, where each role has a specific style of singing, movement, and costume that performers are trained to perform throughout their careers. Therefore, identifying the role category of characters in a play can provide theoretical and systematic foundations for further researches and artistic explorations. By dissecting musical traits and performance styles of each role, comprehensive studies on its regional and artistic nuances are enabled. To achieve role classification in Cantonese opera, we propose RoleNet, an integration network that consists of SincNets, transformers, and a feature fusion block. For a given musical fragment (e.g., an audio signal), SincNets extract 1D features at multiple scales and transformers extract features from time and frequency axes from the 2D Mel Spectrogram. Subsequently, the extracted features are concatenated by the fusioner using multiple features selection strategy to perform role classification tasks. The experimental results on a real-world dataset demonstrated the superior performance of RoleNet compared to single-objective methods. An overall classification accuracy of 98.67% was achieved. The codes are available at https://***/AaronPeng920/RoleNet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.