Application Model Construction of Emotional Expression and Propagation Path of Deep Learning in National Vocal Music

Emotional expression is important in Chinese national vocal music art. The emotional expression in national vocal music is based on the art of national vocal music, with distinct characteristics and requirements. The ultimate goal is to spread the expression of various emotions in the national vocal music art. Promoting the spread of national vocal music singing art using modern media is an urgent requirement for the inheritance and development of national vocal music singing art. With the rapid development of science and technology, integrating deep learning and traditional music has become the general trend. It has been gradually applied to melody recognition, intelligent composition, virtual performance, and other aspects of traditional music and has achieved good results, but also hidden behind a series of ideas and technical and ethical issues. In this paper, the application of deep learning has been discussed and prospected. The recognition rate of emotional expression in national vocal music is 92 %. In terms of communication, combined with the deep learning algorithm, this paper analyzes the characteristics and requirements of emotional expression in the art of national vocal music singing and puts forward a new method of promoting the development of the art of national vocal music singing, hoping to attract more attention and enhance the social awareness of the application field, to promote the steady development of Chinese traditional music in the information age.

@article{Tang2023,
  title     = {Application Model Construction of Emotional Expression and Propagation Path of Deep Learning in National Vocal Music},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Zhangcheng Tang},
  doi       = {10.14569/IJACSA.2023.01411107},
  url       = {https://doi.org/10.14569/IJACSA.2023.01411107}
}