Application of Artificial Intelligence Technology in the Teaching of Complex Situations of Folk Music under the Vision of New Media Art

Abstract

Enhancement in information technology has made the online teaching-learning process easier. However, this process is still a challenging task for teaching courses of theoretical type, music, dance, and arts. For the classes of these types, the traditional system of teaching and learning is well suited, but, in particularly complex situations, accomplishing the task is highly difficult. Hence, new media art technology is introduced to overcome the difficulties. In this research, Chinese folk music is taught online with the aid of new media art with the support of artificial intelligence. The analysis of the proposed work is carried out on the folk music dataset, which considers folk music of ethnic minority groups. A novel Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) art algorithm is implemented to perform the analysis. The performance is compared with the existing gradient descent, Adam, and AdaDelta algorithms. L-BFGS algorithm is essentially a particular recipe for designing and possibly executing an artwork, including algorithms, functions, facial expression, and other input that ultimately decides the structure the folk music and media art would then take. This contribution could be numerical, information processing, or formative. From the obtained results, it can be shown that the proposed system has provided 97% and 98% of accuracy on training and testing data, which is higher when compared to the existing algorithms.