Generative statistical models with self-emergent grammar of chord sequences
Bibliographic Data
| ID | 5304705 |
|---|---|
| Authors | Hiroaki Tsushima (0000-0002-3292-4393, Kyoto University), Eita Nakamura (0000-0003-4097-6027, Kyoto University, corresponding author), Katsutoshi Itoyama (0000-0002-7098-3896, Kyoto University), Kazuyoshi Yoshii (0000-0001-8387-8609, Kyoto University) |
| Year | 2018 |
| Volume | 47 |
| Issue | 3 |
| Pages | 226-248 |
| Publication date | 2018-05-27 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of New Music Research (JOURNAL) |
| Journal identifiers | ISSN: 0929-8215 • E-ISSN: 1744-5027 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/09298215.2018.1447584 |
| OpenAlex | W2745229848 |
| Language | EN |
| Citations received | 1 |
| References cited | 22 |
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
Generative grammar · Grammar · Hidden Markov model · Linguistics · Machine learning · Markov chain · Markov model · Natural language processing · Probabilistic logic · Rule-based machine translation · Statistical model · Animal Vocal Communication and Behavior · Computer Science · Music and Audio Processing · Music Technology and Sound Studies · Artificial Intelligence
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| Citations per year | 0.2 |
|---|---|
| Citation span | 2021 - 2021 (1) |
| Highly cited | No |