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Choreography is an art form in and of itself. Because music and dance have always appeared at the same time throughout human history, music has had a significant influence on dance arrangement. It is important to arrange appropriate dance movements based on the music pieces chosen by users when creating choreography. This paper proposes a mixed density network-based music choreography algorithm in response to the current state of music choreography. The algorithm should be able to convert motion and music signals into a high-level semantic meaning that is compatible with human cognition, compare the degree of matching, and arrange the dance based on the music and motion segments that match. Furthermore, the consistency and authenticity of the movements in the dance created in this paper have been improved. Users’ subjective feedback indicates that the choreography results in this paper are more closely aligned with the music. In the field of music choreography, it has some practical utility.

Music Choreography Algorithm Based on Feature Matching and Fragment Segmentation