Generative Statistical Models with Self-Emergent Grammar of Chord Sequences
Generative Statistical Models with Self-Emergent Grammar of Chord Sequences
复制标题
和弦序列自生语法的生成统计模型
DOI：
10.1080/09298215.2018.1447584
复制
发表时间：
2018
影响因子：
1.1
通讯作者：
Kazuyoshi Yoshii
中科院分区：
文献类型：
--
作者：
Hiroaki Tsushima;Eita Nakamura;Katsutoshi Itoyama;Kazuyoshi Yoshii
和弦序列的生成统计模型在音乐处理中起着至关重要的作用。为了获取特定和弦(如C大调、G和G7、F和DM)之间的句法相似性，我们研究了隐马尔可夫模型和带有潜在变量的概率上下文无关文法模型，这些模型描述了和弦符号的句法类别，以及它们的无监督学习技术，用于从数据中归纳潜在语法。令人惊讶的是，我们发现这些模型在预测能力上往往优于传统的马尔可夫模型，而自涌现类别往往与传统的调和函数相对应。这意味着从信息学的角度来看，和声模型中需要和弦类别。
Generative statistical models of chord sequences play crucial roles in music processing. To capture syntactic similarities among certain chords (e.g. in C major key, between G and G7 and between F and Dm), we study hidden Markov models and probabilistic context-free grammar models with latent variables describing syntactic categories of chord symbols and their unsupervised learning techniques for inducing the latent grammar from data. Surprisingly, we find that these models often outperform conventional Markov models in predictive power, and the self-emergent categories often correspond to traditional harmonic functions. This implies the need for chord categories in harmony models from the informatics perspective.