Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Abstract

Aiming at the problems of low error recognition accuracy and low correlation of data matching in the existing dance action and music beat matching error recognition methods, a dance action and music beat matching error recognition method based on data mining is designed. The quaternion array is used to represent the joint coordinate points of the dance movement, and the coordinates are rotated to obtain the curve change of the dance movement. The dance movement characteristics are regarded as a set of positive and negative sample data, the initial weights of different sample data are calculated, the absolute value is processed for the music beat signal, the interference factors are filtered with the help of Gauss filter, and according to the rhythm law of the music beat signal, the characteristics of music beat signal are extracted and the feature extraction of dance action matching is completed with music beat. The corresponding relationship between dance action and music beat is regarded as the corresponding model, the pairwise occurrence probability between music beat and action is determined, the matching model between dance action and music beat is discretized, and the peak point of correlation data is introduced to complete the matching between dance action and music beat. The features of matching data are extracted and segmented by short-time Fourier transform, the segmented matching data is transformed into matching data, the matching error identification model is established with the help of support vector mechanism, and the constraint conditions of error detection are set to complete the matching error identification. The experimental results show that the proposed method has high recognition accuracy and high data matching correlation.

A Literature Review on Music Parameter Extraction and Visualization

“…Pinto et al (2021) proposed a real-world beat-tracking strategy based on a relatively small temporal region of annotated beat positions and focused fine-tuning of the most advanced deep neural network to extract beats from music audio signals. To improve the accuracy and relevance of beat matching, Zhu (2022) developed a data mining-based error recognition system for dance movement and music beat matching.…”

Retracted: Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

“…Security and Communication Networks has retracted the article titled "Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining" [1] due to concerns that the peer review process has been compromised.…”