“Research on music emotion analysis and dance creation based on neural network”

Abstract

The one-of-a-kind avenues for self-expression that dancing provides as a form of physical art. Movements of the body, both internal and external, and other forms of expression make up the bulk of a dancer's lexicon. Through passive observation, the viewer is able to catch up on the dancer's intended meaning through their body language. It's the one-of-a-kind way that each dancer finds to convey feeling and story through their body. In this piece, we intelligently oversee dance training and take on challenging nonlinear control problems by combining AI technology and the BP neural networks (BPNN) approach.In this study, the training dance teachers receive in dance language, talking music for dancing, and stage art was evaluated using the BPNN technique and the PCA-BPNN strategy. Median accuracy for the BPNN research model is 85.35 percent as age 80 approaches, whereas for the PCA-BPNN research model it is just 65.64 percent. This demonstrates that the BPNN grading model provides more precise results than the PCA-BPNN model. Using BPNN algorithm-based dancing performed within the context of artificial intelligence technology is one way to attain two goals: the spread and pleasure of beauty, and the harmonious combination of athletics and the arts. These two aims are not mutually exclusive.