Matching Model of Dance Movements and Music Rhythm Features Using Human Posture Estimation

Abstract

The essential issue in music understanding and dance synthesis research is how to improve the degree of matching between dance motions and music rhythm elements. The matching model of dance movements and music rhythm features is created in this study based on human posture estimation research to tackle the problems that existing matching methods are prone to rapid changes in movements and cannot keep the original movement features. The rhythm properties of movement and music data are first analyzed, and then the degree of matching between movement and music pieces is measured using a dynamic time warping technique. A constraint-based dynamic planning approach is also used to synthesize the dance action sequence that best matches the supplied music. Experiments reveal that this model has a matching accuracy of 95.1 percent, which is higher than the two comparative models’ matching accuracy of 5.2 percent and 7.6 percent, respectively. The matching score of this paper is high, reaching around 94 percent, according to the user research results. The method suggested in this work has apparent advantages in that it may efficiently help users in the arrangement of desired dance moves or backing music, and it can be applied in the field of actual choreography and scoring.