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多音乐环境中音乐创作的自我心理控制与创造力

Self-Psychological Control and Creativity of Music Creation in Multimusic Environment.

作者信息

Xu Caihong

机构信息

The Department of Music, Yuncheng University, Yuncheng, Shanxi, China.

出版信息

J Environ Public Health. 2022 Jul 8;2022:7849909. doi: 10.1155/2022/7849909. eCollection 2022.

Abstract

This study aimed to solve the difficulties in the research of self-psychology, three executive controls, and creativity in multimusic environment. In today's teaching and performance, the study of piano adaptation has practical significance in broadening artistic vision, improving performance technology, and training symphonic music thinking. A research method of self-psychological three executive controls and creativity generation for creation in multimusic environment is proposed. A computer composition algorithm is based on the hidden Markov model and interactive genetic algorithm. By integrating melody elements and rhythm into the traditional Markov model with only notes or rhythm as the unit, a new hidden Markov music prediction model is established, and the interactive genetic algorithm is used to optimize the music segments. Simulation results show that the algorithm can use a small dataset to quickly create music clips with certain melody logic and in line with users' personality. It is proved that the method based on the hidden Markov model and interactive genetic algorithm can meet the research needs of self-psychological three execution control and creativity generation in multimusic environment.

摘要

本研究旨在解决多音乐环境下自我心理学、三种执行控制和创造力研究中的困难。在当今的教学与表演中,钢琴改编研究对于拓宽艺术视野、提高表演技术以及培养交响乐思维具有现实意义。提出了一种在多音乐环境下进行自我心理三种执行控制及创造力生成创作的研究方法。一种基于隐马尔可夫模型和交互式遗传算法的计算机作曲算法。通过将旋律元素和节奏融入仅以音符或节奏为单元的传统马尔可夫模型中,建立了一种新的隐马尔可夫音乐预测模型,并利用交互式遗传算法对音乐片段进行优化。仿真结果表明,该算法能够利用小数据集快速创作具有一定旋律逻辑且符合用户个性的音乐片段。证明了基于隐马尔可夫模型和交互式遗传算法的方法能够满足多音乐环境下自我心理三种执行控制及创造力生成的研究需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97db/9286894/2d089288cf25/JEPH2022-7849909.001.jpg

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