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Music significantly influences dance performances by shaping the overall mood and energy. However, the challenge of selecting appropriate tracks that seamlessly align with diverse dance styles often hampers choreographers’ creative expression and audience engagement. The objective of the study is to develop an intelligent system that utilizes music information retrieval (MIR) and artificial intelligence (AI) techniques to provide music selection and matching suggestions for dance creations. The study collects data from musical tracks, which included various genres and styles tailored to different dance forms. Initially collected data are preprocessed to enhance quality and remove noise. Feature extraction is performed using convolutional neural networks (CNNs), which analyze time-frequency representations of the music to capture relevant musical features. This feature extraction process is integral to the MIR framework, enabling the system to discern patterns and attributes with the music. A suggestion system is then developed, utilizing the extracted features to match music tracks to dance styles. The study proposed a derivative-free optimized refined random forest (DFO-RRF) method that effectively enhances model performance by fine-tuning hyperparameters without gradient calculations and improves accuracy in matching music tracks to dance styles through efficient feature utilization and model performance tuning. The result demonstrated a significant improvement in music-dance matching accuracy compared to traditional methods. The DFO-RRF method, which is the recommended approach, contains the highest peak results in terms of accuracy (96%), recall (96%), maximum load system (2550), and recommendation error (2.5). The integration of music information retrieval (MIR) and derivative-free optimized refined random forest (DFO-RRF) in this study provides a novel and innovative approach to music selection and matching for dance, thereby significantly enhancing the choreographic process. The study enhances dancers’ and choreographers’ creative possibilities by providing music suggestions based on their performances, streamlining the duration process.
Wu et al. (Mon,) studied this question.