Detection of Context-free Grammar in Music with Evolutionalry Linguistics

2015 Fiscal Year Final Research Report

• Project/Area Number: 25330434
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Entertainment and game informatics 1
• Research Institution: Japan Advanced Institute of Science and Technology
• Principal Investigator: Tojo Satoshi 北陸先端科学技術大学院大学, 情報科学研究科, 教授 (90272989)
• Co-Investigator(Kenkyū-buntansha): ONO Tetsuo 北海道大学, 情報科学研究科, 教授 (40343389) ; UEDA Kazuhiro 東京大学, 大学院情報学環, 教授 (60262101) ; HASHIMOTO Takashi 北陸先端科学技術大学院大学, 知識科学研究科, 教授 (90313709) ; HIRATA Keiji はこだて未来大学, システム情報科学部, 教授 (30396121)
• Project Period (FY): 2013-04-01 – 2016-03-31
• Keywords: 音楽理論 / 文脈自由文法 / カデンツ / 繰り返し学習モデル / GTTM / TPS

Outline of Final Research Achievements

There exist context-free grammar (CFG) rules in music, e.g.,for sequences of chords as cadences. In this research, we have investigated the formalisms to detect such CFG rules in music from two different aspects. First, we relied on the Generative Theory of Tonal Music (GTTM) and found a long-distance dependency, that is a witness for being CFG, in its time-span trees. Second, we have tried a method on evolutionary linguistics called the iterated learning model (ILM), and improved the efficiency of the grammar detection. In time-span tree, we introduced the notion of pitch, and thus we could detect the most plausible sequence of chords, employing the theory of tonal pitch space (TPS). In addition, we have defined the distance between two time-span trees, by the sum of different time-spans, and then we have evaluated the correlation between this distance and our psychological similarity. As for ILM, we have invented a method of string clipping, and thus we could improve the efficiency.

Free Research Field

人工知能