Presentation 2022-12-21
Density-based Bias-Free Automatic Chart Generation for Rhythm Games
Zhao Yifan, Tsunenori Mine,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Automatic chart generation methods for rhythm games often use both acoustic features and difficulty information when building models to predict the onset of the chart. Since difficulty information relates to both onset placement and the key structure of the game, direct use of difficulty information may cause unnecessary information bias in onset prediction. In this paper, we propose an approach that calculates density information to generate charts of different difficulty levels and uses the calculated information as an attribute for each level of onset. This allows the model to learn onset distributions with different levels of difficulty without directly using the difficulty information. Furthermore, integrating density-based onset sequences into a single one improves prediction performance, and density information can also be used to filter charts of different difficulty levels from the integrated chart.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Automatic Content GenerationVideo GameMachine Learning
Paper # AI2022-35
Date of Issue 2022-12-14 (AI)

Conference Information
Committee AI
Conference Date 2022/12/21(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Yuichi Sei(Univ. of Electro-Comm.)
Vice Chair Yuko Sakurai(AIST) / Tadachika Ozono(Nagoya Inst. of Tech.)
Secretary Yuko Sakurai(Tokyo Univ. of Agriculture and Technology) / Tadachika Ozono(Toho Univ.)
Assistant Kazutaka Matsuzaki(Chuo Univ.)

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Density-based Bias-Free Automatic Chart Generation for Rhythm Games
Sub Title (in English)
Keyword(1) Automatic Content GenerationVideo GameMachine Learning
1st Author's Name Zhao Yifan
1st Author's Affiliation Kyushu University(Kyushu Univ.)
2nd Author's Name Tsunenori Mine
2nd Author's Affiliation Kyushu University(Kyushu Univ.)
Date 2022-12-21
Paper # AI2022-35
Volume (vol) vol.122
Number (no) AI-322
Page pp.pp.13-17(AI),
#Pages 5
Date of Issue 2022-12-14 (AI)