{"updated":"2025-01-19T21:44:19.117346+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00199413","sets":["6164:6165:7308:9903"]},"path":["9903"],"owner":"44499","recid":"199413","title":["ダンスゲーム譜面の特性分析とクラスタリングに基づく特徴的な譜面の自動生成"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-09-13"},"_buckets":{"deposit":"c6fe94f3-107a-4fbf-adeb-6beb3bb2eb2f"},"_deposit":{"id":"199413","pid":{"type":"depid","value":"199413","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ダンスゲーム譜面の特性分析とクラスタリングに基づく特徴的な譜面の自動生成","author_link":["482499","482501","482500","482498"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ダンスゲーム譜面の特性分析とクラスタリングに基づく特徴的な譜面の自動生成"},{"subitem_title":"Generation of Characteristic Charts Based on Analyzing and Clustering Charts of Dance Games","subitem_title_language":"en"}]},"item_type_id":"18","publish_date":"2019-09-13","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"立命館大学大学院"},{"subitem_text_value":"立命館大学"},{"subitem_text_value":"立命館大学"},{"subitem_text_value":"立命館大学"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/199413/files/IPSJ-EC2019020.pdf","label":"IPSJ-EC2019020.pdf"},"date":[{"dateType":"Available","dateValue":"2019-09-13"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-EC2019020.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"40"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"bc6377c1-a771-4ce4-95d0-3e950fcd516f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"辻野, 雄大"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山西, 良典"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山下, 洋一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"井本, 桂右"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ダンスゲーム譜面は，操作を要求される頻度，リズム構成の複雑さなど，複数の要素によって特徴づけられる．これらの要素によって，ダンスゲーム譜面の難しさおよび「面白さ」に影響する特性が形成される．この特性を客観的かつ多次元で表現する特徴量を提案する．提案した特徴量に基づいてk-means法によるクラスタリングを実施し，特性が類似した譜面のクラスタを得る．クラスタ毎に楽曲と譜面の関係を深層学習させることによって，クラスタの特性を備えた譜面の自動生成が可能となり，多様な「面白さ」に対応した特徴的な譜面の提供を実現した．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"103","bibliographic_titles":[{"bibliographic_title":"エンタテインメントコンピューティングシンポジウム2019論文集"}],"bibliographicPageStart":"96","bibliographicIssueDates":{"bibliographicIssueDate":"2019-09-13","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2019"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:03:19.837878+00:00","id":199413,"links":{}}