IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i5p2923-d762704.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/5/2923/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/5/2923/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chenghong Cen & Guang Luo & Ye Tian & Boyu Fu & Yuxin Chen & Shanglan Huang & Tan Jiang & Guanghui Huang, 2024. "Enhancing the dissemination of Cantonese Opera among youth via Bilibili: a study on intangible cultural heritage transmission," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jie Chen & Zhen Guo & Shaogui Xu & Rob Law & Chenmei Liao & Wenqin He & Mu Zhang, 2022. "A Bibliometric Analysis of Research on Intangible Cultural Heritage Tourism Using CiteSpace: The Perspective of China," Land, MDPI, vol. 11(12), pages 1-17, December.
    2. Gege Zhang & Xiaoyuan Chen & Rob Law & Mu Zhang, 2020. "Sustainability of Heritage Tourism: A Structural Perspective from Cultural Identity and Consumption Intention," Sustainability, MDPI, vol. 12(21), pages 1-17, November.
    3. Huan Sun & Shaofeng Wu & Bolun Zhang, 2023. "Energy Literacy of Residents in Rural Communities: Comparison of Tourism and Non-Tourism Villages," Energies, MDPI, vol. 16(20), pages 1-17, October.
    4. Zhichong Ma & Yangyang Guo, 2024. "Leveraging Intangible Cultural Heritage Resources for Advancing China’s Knowledge-Based Economy," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 12946-12978, September.
    5. Hongmei Xia & Yanling Li & Fang Chen & Bo Xu, 2022. "The Influence of Participant Subject Factors on Collaboration Effects in the Protection of China’s ICH: The Mediating Role of Relationship Quality," Sustainability, MDPI, vol. 14(3), pages 1-19, January.
    6. Wenwen Lu & Yongjun Su & Sitan Su & Jie Zhao & Li Zhang, 2022. "Perceived Authenticity and Experience Quality in Intangible Cultural Heritage Tourism: The Case of Kunqu Opera in China," Sustainability, MDPI, vol. 14(5), pages 1-17, March.
    7. Jianrong Yao & Xiangliang Guo & Lu Wang & Hui Jiang, 2022. "Understanding Green Consumption: A Literature Review Based on Factor Analysis and Bibliometric Method," Sustainability, MDPI, vol. 14(14), pages 1-13, July.
    8. Lekai Zhang & Ming Li & Lingyan Zhang & Xiaojian Liu & Zhichuan Tang & Yingfan Wang, 2022. "MasterSu: The Sustainable Development of Su Embroidery Based on Digital Technology," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    9. Wei Wang & Yun Gao & Adrian Pitts & Lili Dong, 2023. "A Bibliometric Analysis of Neighborhood Sense of Community," Sustainability, MDPI, vol. 15(5), pages 1-18, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2923-:d:762704. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.