{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00227343","sets":["1164:5064:11199:11312"]},"path":["11312"],"owner":"44499","recid":"227343","title":["計算論的暗意-実現モデルに向けた旋律における暗意-実現/否定構造の統計的分析とラベル列の階層化"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-08-20"},"_buckets":{"deposit":"b28d8e37-2e49-49fe-8247-9da88dc2759c"},"_deposit":{"id":"227343","pid":{"type":"depid","value":"227343","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"計算論的暗意-実現モデルに向けた旋律における暗意-実現/否定構造の統計的分析とラベル列の階層化","author_link":["605446","605445","605444","605447"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"計算論的暗意-実現モデルに向けた旋律における暗意-実現/否定構造の統計的分析とラベル列の階層化"},{"subitem_title":"Statistical Analysis of Melodic Implication-Realization/Denial Structures and Hierarchization of Label Sequences towards Computational Implication-Realization Model","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"音楽分析","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2023-08-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"公立はこだて未来大学大学院"},{"subitem_text_value":"公立はこだて未来大学大学院"},{"subitem_text_value":"公立はこだて未来大学大学院"},{"subitem_text_value":"公立はこだて未来大学大学院"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/227343/files/IPSJ-MUS23138004.pdf","label":"IPSJ-MUS23138004.pdf"},"date":[{"dateType":"Available","dateValue":"2025-08-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MUS23138004.pdf","filesize":[{"value":"786.6 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"21"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"1329df42-98b6-4ef1-afe7-c12ffbbfaa1f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"釡石, 健太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"能登, 楓"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹川, 佳成"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"平田, 圭二"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10438388","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8752","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では，旋律における大域的な暗意（予測）も含めて分析するための暗意-実現モデルの拡張について述べる．暗意-実現モデルは，仮説として表現された 2 つの暗意発生のルールに基づき，音楽聴取時に生じる暗意の実現/否定を分析する音楽理論である．暗意-実現モデルによる旋律の分析は，数音単位で I-R シンボルと呼ばれるラベルを割り当てることで行われる．そのため，オリジナルの暗意-実現モデルでは，数音にまたがって生じる局所的な暗意-実現/否定の分析のみが可能であった．しかし音楽には，小節や楽節を単位とするようなより大域的な暗意-実現/否定構造が存在する．さらに，オリジナルの暗意-実現モデルの分析過程には，コンピュータで実装する上で曖昧な部分があり，その部分は研究者によって異なる実装が行われてきた．本研究では，大域的な暗意-実現/否定を計算機上で分析するために，数音よりも大きい時間幅での旋律へのラベリングと，ラベル列の統計的学習による暗意発生ルールの抽出を行った．ラベリングは I-R シンボル列のクラスタリングとラベル列の階層化，ラベル列の学習は可変長 n-gram モデルにより行った．学習された n-gram 確率を確認した結果，暗意発生のルールがオリジナルの仮説を一般化した形で抽出されたことが確認できた．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告音楽情報科学（MUS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-08-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicVolumeNumber":"2023-MUS-138"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:26:36.634304+00:00","updated":"2025-01-19T12:12:14.474557+00:00","id":227343}