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

Qiao Chen,Qin Wang,Wenfeng Zhao,Yawen Zhao

doi:10.3390/su14052923

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% F1 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.

Highlights

Cantonese opera, which is a representative genre of drama in Guangdong Province in China, was selected as one of the first representative lists of China’s national intangible cultural heritage in 2006 and was included in the UNESCO Representative List of the Intangible Cultural Heritage of Humanity in 2009
The learning rate was set to 0.00001, the batch size was set to 128, the ratio of the training set to the validation set was 7:3, the categorical cross-entropy loss function was used as the model training performance indicator, and the adaptive moment estimation (Adam) optimizer was utilized for parameter adjustment and optimization during the training process
This paper mainly involves the scientific and standardized construction of a database of Cantonese opera audio data and the use of deep learning technique to achieve an intelligent classification of Cantonese opera singing genres

Summary

Introduction

Cantonese opera, which is a representative genre of drama in Guangdong Province in China, was selected as one of the first representative lists of China’s national intangible cultural heritage in 2006 and was included in the UNESCO Representative List of the Intangible Cultural Heritage of Humanity in 2009. As a style of drama sung in Cantonese, Cantonese opera has a history of over 300 years, showing both deep traditional cultural heritage and strong Chinese identity. Cultural, social and economic values are culturally and viscerally supportive to the development of cultural and creative industries. The sustainable development of Cantonese opera can lead to a more scientific and standardized heritage and innovation of Cantonese opera culture, and promote the flourishment of cultural and creative industries. Taking advantage of artificial intelligence technology to rescue, excavate, organize, protect and disseminate traditional culture has become the direction of intangible cultural heritage conservation today [1,2]. Studying the intelligent development of intangible cultural heritage will become an Sustainability 2022, 14, 2923.

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