{"created":"2025-01-19T01:29:33.665600+00:00","updated":"2025-01-19T11:18:52.589021+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230039","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230039","title":["ピアノ譜から吹奏楽譜への楽器編成を指定可能な自動編曲"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"755af008-fce9-4153-a538-a3b7bdebb9d1"},"_deposit":{"id":"230039","pid":{"type":"depid","value":"230039","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ピアノ譜から吹奏楽譜への楽器編成を指定可能な自動編曲","author_link":["618899","618898","618897","618896"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ピアノ譜から吹奏楽譜への楽器編成を指定可能な自動編曲"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"京大"},{"subitem_text_value":"京大"},{"subitem_text_value":"京大"},{"subitem_text_value":"京大"}]},"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/230039/files/IPSJ-Z85-1T-04.pdf","label":"IPSJ-Z85-1T-04.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-1T-04.pdf","filesize":[{"value":"1.8 MB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"296b773a-6d53-48ff-bc19-be6d8c70ead5","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"鍋岡, 琢渡"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 栄太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"吉井, 和佳"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"寺尾, 萌夢"}],"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_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では、ピアノ譜からユーザーが指定した楽器編成の吹奏楽譜を生成する手法を構築する。単純には、ピアノ譜を吹奏楽譜に変換する深層ニューラルネットワークを楽器編成条件付きで教師あり学習する方法が考えられる。しかし、正確に対応の取れたペアデータは入手が困難な上、数十にも及ぶ楽器パートの音域や特性に加えて、楽器パート間の同時的・経時的な関係性を学習する必要があるため、本タスクではデータ不足の問題が深刻である。そこで、既存の自動編曲手法を用いて吹奏楽譜をピアノ曲に変換し、それらをペアデータとして学習に用いることを試みる。さらに、使用頻度が低い楽器パートの品質向上のための学習の改善方法を比較検討する。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"490","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"489","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230039,"links":{}}