Chord序列的自涌现文法生成统计模型
Generative Statistical Models with Self-Emergent Grammar of Chord Sequences
摘要：
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。为了捕捉某些和弦之间的句法相似性（例如，在C大调，G和G7之间，F和Dm之间），我们研究隐马尔可夫模型和概率上下文无关文法模型与潜在变量描述的和弦符号的句法类别和它们的无监督学习技术，从数据中归纳出潜在的语法。令人惊讶的是，我们发现，这些模型往往优于传统的马尔可夫模型的预测能力，和自我涌现的类别往往对应于传统的调和函数。这意味着从信息学的角度来看，和声模型中需要和弦类别。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
