2022 Fiscal Year Final Research Report
Performance Information Analysis for Creating Performance Expression Model of Classical Piano Music
| Project/Area Number |
20K12119
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 62040:Entertainment and game informatics-related
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| Research Institution | University of Tsukuba |
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Principal Investigator |
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | 音楽情報学 / 演奏表情 / 演奏分析 / オンセット検出 / 暗意実現モデル / クラシック音楽 |
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| Outline of Final Research Achievements |
I have analyzed performance information for the generation of performance expression models of classical piano music and studied the technologies required for the analysis. Specifically, I have designed a prototype of a data cleansing system for extracting performance expressions from music performance information and music score information. And I have studied methods to reduce the computational complexity of accurately detecting the onset of sound in waveform data, as well as the robustness and accuracy of the system.I have also analyzed performance expressions based on implicit realization models instead of musical models such as GTTM and TPS, which are precise but computationally expensive and require in-depth musical knowledge.
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| Free Research Field |
音楽情報科学
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| Academic Significance and Societal Importance of the Research Achievements |
本研究ではクラシックのピアノ音楽に関して，演奏表情を主に強弱と緩急の変化で表現されるものととらえ，その演奏表情モデルを解析した．演奏表情モデルを明らかにすることで，音楽鑑賞および演奏への理解が深まる．また，本研究で特に重要視した「オンセット検出」は，演奏表情を導出ための基本的概念であり，この検出が瞬時に正確に容易に行うことができれば豊かな表情のついた自動演奏などの応用範囲が格段に広がる．
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