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