Generative statistical models with self-emergent grammar of chord sequences. Issue 3 (27th May 2018)

• Record Type: Journal Article
• Authors: Tsushima, Hiroaki; Nakamura, Eita; Itoyama, Katsutoshi; Yoshii, Kazuyoshi
• Is Part Of: Journal of new music research. Volume 47:Issue 3(2018)
• Page Start: 226
• Page End: 248
• Publication Date: 2018-05-27
• Subjects: Statistical music language model -- harmonic (tonal) function -- unsupervised grammar induction -- Bayesian inference -- hidden Markov model -- probabilistic context-free grammar; Music -- Periodicals; Musicology -- Periodicals; 780.72
• DOI: 10.1080/09298215.2018.1447584
• Languages: English
• ISSNs: 0929-8215

Abstract

ABSTRACT: 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.