Optimization Simulation of Dance Technical Movements and Music Matching Based on Multifeature Fusion

Abstract

Music and dance videos have been popular among researchers in recent years. Music is one of the most important forms of human communication; it carries a wealth of emotional information, and it is studied using computer tools. In the feature engineering process, most present machine learning approaches suffer from information loss or insufficient extracted features despite the relevance of computer interface and multimedia technologies in sound and music matching tasks. Multifeature fusion is widely utilized in education, aerospace, intelligent transportation, biomedicine, and other fields, and it plays a critical part in how humans get information. In this research, we offer an effective simulation method for matching dance technique movements with music based on multifeature fusion. The initial step is to use music beat extraction theory to segment the synchronized dance movements and music data, then locate mutation points in the music, and dynamically update the pheromones based on the merits of the dance motions. The audio feature sequence is obtained by extracting audio features from the dancing video’s accompanying music. Then, we combine the two sequences to create an entropy value sequence based on audio variations. By comparing the consistency of several approaches for optimizing dance movement simulation trials, the optimized simulation method described in this research has an average consistency of 87%, indicating a high consistency. As a result, even though the background and the subject are readily confused, the algorithm in this research can keep a consistent recognition rate for more complicated dance background music, and the approach in this study can still guarantee a certain accuracy rate.

Music Tone Synthesis based Anti-Interference Dynamic Integral Neural Network optimized with Artificial Hummingbird Optimization Algorithm

“…Two essential pieces of technological equipment for the cepstral area assessment are the Mel frequency cepstral factor and the straight predictive cepstral factor. Simultaneously, the music education curriculum is heavily influenced by noise and leads to the existing audio classifiers implementation that fall short of potential needs [13][14][15]. Most of the common methods of research depend on traditional signal processing techniques and a less study on music appreciation utilizing deep neural networks and much growth potential in the appreciation precision along productivity [16].…”