保护印度丰富的舞蹈遗产:印度舞蹈形式的分类和文化遗产保护的创新数字管理解决方案

R. Tiwari, Vinay Gautam, Vikrant Sharma, A. Jain, N. K. Trivedi
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引用次数: 0

摘要

舞蹈与文化遗产之间存在着深刻的联系。舞蹈作为一种文化特征的重要组成部分,作为维护和尊重该文化独特传统和习俗的一种手段,往往代代相传。一种文化的历史、信仰和价值观可以通过舞蹈有力地表达出来,舞蹈也可以用于交流和讲故事。为了保护和促进印度的文化遗产,了解印度舞蹈风格的分类是至关重要的。本文提出了一种混合卷积神经网络(CNN)和循环神经网络(RNN)深度学习方法,用于印度舞蹈风格类别的准确分类。该模型结合了CNN和RNN的优势,分别利用了空间和时间信息,从而提高了性能和精度。进行了大量的实验来评估所提出的方法的性能。结果表明,混合CNN-RNN模型的准确率达到了97.74%,优于传统方法和单模型架构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preserving India’s Rich Dance Heritage: A Classification of Indian Dance Forms and Innovative Digital Management Solutions for Cultural Heritage Conservation
Deep connections exist between dance and cultural heritage. Dance is frequently passed down through generations as an essential component of a culture’s identity and as a means of maintaining and honoring that culture’s distinctive traditions and customs. A culture’s history, beliefs, and values can be powerfully expressed via dance, which can also be used for communication and storytelling. For the purpose of protecting and promoting India’s cultural legacy, it is crucial to comprehend how Indian dance styles are categorized. This paper proposes a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) deep learning approach for the accurate classification of Indian dance style categories. The proposed model combines the strengths of both CNN and RNN to leverage spatial and temporal information, respectively, resulting in enhanced performance and improved accuracy. Extensive experiments were conducted to evaluate the performance of the proposed approach. The results demonstrate that the hybrid CNN-RNN model achieved an impressive accuracy of 97.74%, outperforming traditional methods and single-model architectures.
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