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The Sustainable Development of Intangible Cultural Heritage with AI: Cantonese Opera Singing Genre Classification Based on CoGCNet Model in China

Author

Listed:
  • Qiao Chen

    (College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China)

  • Wenfeng Zhao

    (College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
    South China Smart Agriculture Public R&D (Research & Development) Platform, Ministry of Agriculture and Rural Affairs, Guangzhou 510520, China)

  • Qin Wang

    (Guangdong Art Research Institute, Guangzhou 510075, China)

  • Yawen Zhao

    (College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China)

Abstract

Chinese Cantonese opera, a UNESCO Intangible Cultural Heritage (ICH) of Humanity, has faced a series of development problems due to diversified entertainment and emerging cultures. While, the management on Cantonese opera data in a scientific manner is conducive to the sustainable development of ICH. Therefore, in this study, a scientific and standardized audio database dedicated to Cantonese opera is established, and a classification method for Cantonese opera singing genres based on the Cantonese opera Genre Classification Networks (CoGCNet) model is proposed given the similarity of the rhythm characteristics of different Cantonese opera singing genres. The original signal of Cantonese opera singing is pre-processed to obtain the Mel-Frequency Cepstrum as the input of the model. The cascade fusion CNN combines each segment’s shallow and deep features; the double-layer LSTM and CNN hybrid network enhance the contextual relevance between signals. This achieves intelligent classification management of Cantonese opera data, meanwhile effectively solving the problem that existing methods are difficult to classify accurately. Experimental results on the customized Cantonese opera dataset show that the method has high classification accuracy with 95.69% Precision, 95.58% Recall and 95.60% F 1 value, and the overall performance is better than that of the commonly used neural network models. In addition, this method also provides a new feasible idea for the sustainable development of the study on the singing characteristics of the Cantonese opera genres.

Suggested Citation

  • Qiao Chen & Wenfeng Zhao & Qin Wang & Yawen Zhao, 2022. "The Sustainable Development of Intangible Cultural Heritage with AI: Cantonese Opera Singing Genre Classification Based on CoGCNet Model in China," Sustainability, MDPI, vol. 14(5), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2923-:d:762704
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    References listed on IDEAS

    as
    1. Qiong Dang & Zhongming Luo & Chuhao Ouyang & Lin Wang & Mei Xie, 2021. "Intangible Cultural Heritage in China: A Visual Analysis of Research Hotspots, Frontiers, and Trends Using CiteSpace," Sustainability, MDPI, vol. 13(17), pages 1-21, September.
    2. Hongmei Xia & Tong Chen & Guanghui Hou, 2020. "Study on Collaboration Intentions and Behaviors of Public Participation in the Inheritance of ICH Based on an Extended Theory of Planned Behavior," Sustainability, MDPI, vol. 12(11), pages 1-15, May.
    3. Taniya Hasija & Virender Kadyan & Kalpna Guleria & Abdullah Alharbi & Hashem Alyami & Nitin Goyal, 2022. "Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System," Sustainability, MDPI, vol. 14(2), pages 1-22, January.
    Full references (including those not matched with items on IDEAS)

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