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遗传算法在辅助作曲中的交互作用研究。

Research on the Interaction of Genetic Algorithm in Assisted Composition.

机构信息

School of Architecture and Art, Central South University, Changsha, Hunan 410006, China.

出版信息

Comput Intell Neurosci. 2021 Nov 22;2021:3137666. doi: 10.1155/2021/3137666. eCollection 2021.

DOI:10.1155/2021/3137666
PMID:34853584
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8629674/
Abstract

Computer-aided composition is an attempt to use a formalized process to minimize human (or composer) involvement in the creation of music using a computer. Exploring the problem of computer-aided composition can enable us to understand and simulate the thinking mode of composers in the special process of music creation, which is an important application of artificial intelligence in the field of art. Feature extraction on the MIDI files has been introduced in this paper. Based on the genetic algorithm in this paper, a platform of the sampling coding method to optimize the character representation has solved the traditional algorithmic music composition study. Music directly from the pitch and duration can be derived from the characteristics, respectively, in the form of a one-hot encoding independently said. Failure to the rhythm of the characterization of the pitch and duration are problems that lead to the inability of compositional networks to learn musical styles better. Rhythm is the combination of pitch and time values according to certain rules. The rhythm of music affects the overall style of music. By associating the pitch and time value coding, the rhythm style of music can be preserved better so that the composition network can learn the style characteristics of music more easily.

摘要

计算机辅助作曲是指使用计算机，通过形式化的过程，尽可能减少人（或作曲家）对音乐创作的参与，试图进行音乐创作的一种方式。探索计算机辅助作曲的问题，能够使我们理解和模拟作曲家在音乐创作的特殊过程中的思维模式，这是人工智能在艺术领域的一个重要应用。本文在 MIDI 文件上进行了特征提取。基于本文中的遗传算法，一个采样编码方法的优化特征表示的平台，解决了传统算法音乐作曲研究中的问题。音乐可以直接从音高和持续时间的特征中推导出来，分别以独热编码的形式独立表示。未能对音高和持续时间的节奏进行特征化是导致组合网络无法更好地学习音乐风格的问题。节奏是根据一定规则组合音高和时值的方式。音乐的节奏影响着音乐的整体风格。通过关联音高和时值编码，可以更好地保留音乐的节奏风格，使作曲网络更容易学习音乐的风格特征。

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