1. 基于动态频域分解的乐队指挥动作生成.
- Author
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贺鑫, 刘凡, 陈德龙, and 周睿志
- Abstract
In recent years, the intrinsic relationship between music and motions have been widely studied. However,very few efforts have been made to develop music-driven conducting motion generation models, which takes music as input signal to generate conducting motion in harmony with music rhythm and semantics. In this paper, a music-driven conducting motion generation approach based on dynamic frequency-domain motion decomposition (DFMD) was proposed. Specifically, a filter is first constructed using the beat information to decompose the command action into high and low frequency components. Then, a deep convolutional neural network dynamically learns these components,and it synthesizes the final command action. Experimental results on the large-scale ConductorMotion100 dataset show that the standard deviation of the generated low-frequency and high-frequency motion components is 4.4579 and 9.6466,which are very close to the real motions. The proposed method breaks through the limitations of coherence and coordination in time-domain or spatial-domain motion decomposition, and effectively avoids the influence of large-value low-frequency motion on small-value high-frequency motion. The visualized results show that the generated movements are natural, beautiful, diverse, and closely synchronized with the music signal. It provides a new understanding of the connection between music and movement, and brings innovative application prospects to the field of musical performance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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