A Model of Music Perceptual Theory Based on Markov Chains

被引：0

作者：

Wen, Ru ^{[1
]}

Chen, Kai ^{[2
]}

Zhang, Yilin ^{[3
]}

Huang, Wenmin ^{[3
]}

Tian, Jiyuan ^{[3
]}

Xu, Kuan ^{[3
]}

Wu, Jiang ^{[3
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Humanities & Social Sci, Xian 710049, Peoples R China

[2] Rutgers State Univ, Sch Arts & Sci, New Brunswick, NJ 08901 USA

[3] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China

来源：

PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC) | 2018年

关键词：

Music style analysis; Markov chain; Implication-Realization theory; clustering; machine learning;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Music perceptions can be regarded as the expectancies for which the brain processes temporal statistics to predict future rhythms. The different patterns of the expectancy streams represent the different styles of music. In this paper, we present a model for music style recognition based on a music cognitive theory combined with machine learning approach. First, we establish a Markov chain with eight states where each state represents a certain composition mode derived from the Implication-Realization (IR) theory. Then we use a clustering method to detect music styles hidden in compositions. The results are identical to the conclusions in musicology, which confirms the effectiveness of our method.

引用

页码：1099 / 1105

页数：7

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