Music-oriented choreography considering hybrid density multimedia network algorithms

Abstract

Music-oriented choreography combines dance sequences with musical compositions to enhance artistic experience. With multimedia network complexity and data management, advanced algorithms are being used to optimize real-time choreography. Traditional choreography relies on manual adjustments, but multimedia technologies can process vast data efficiently. This paper introduces the Inspired Adam Optimized-Hybrid Density Networks (IAO-HDN), a novel algorithm that automates the creation of dance routines tailored to specific music. The process involves three main steps: motion screening, feature matching, and motion generation. At first, choreography video data was gathered for dance movements and corresponding music features, such as tempo and rhythm. The video data are preprocessed by the normalization method, and then the normalized data is employed in Principal Component Analysis (PCA) to extract their features. HDNs for modeling complex distributions of dance pose relative to music feature sets, by IAO fine-tune the parameters of the IAO-HDN model for better performance in generating and synchronizing dance routines with music. The IAO-HDN stimulated using TensorFlow for creating dance routines that match the music, making the process more efficient and improving the quality of the choreography. The IAO-HDN produces dance movements that align more effectively with the music, showcasing more realistic and coherent transitions than previous methods. The study generates and synchronizes dance routines with music, and the results demonstrate accuracy (89%), precision (85%), recall (80%), and F1-score (82%), indicating improvements in movement generation and the alignment of dance with musical elements.