Exploration of Dance Choreography Strategies Based on Big Data Information Analysis Technology

Abstract

Intelligent choreography is the inevitable direction of the intelligent development of dance art. At present, there is the problem of insufficient accuracy of choreography feature extraction. This paper proposes an automatic music choreography algorithm combined with big data. Based on a large number of existing music and dance data, this algorithm uses machine learning algorithm to train the model, which can automatically and intelligently generate desired dance movements in combination with screening conditions. Moreover, combined with the idea of data mining, this paper puts forward a data generation strategy, which randomly adds irregular disturbances at the time series level and spatial level to the professional dance sequences in the data set, synthesizes non-professional dances, and constructs a training set that meets the requirements in scale and choreography complexity. In addition, this paper designs a two-stage framework: dance-music alignment stage. Through the analysis of the results, it can be seen that the choreography method combined with feature extraction proposed in this paper can effectively identify various dance features. In particular, it can learn from a large number of dance features, effectively improve the quality of dance choreography, and play a certain role in promoting the further development of subsequent dance choreography.