{"created":"2025-01-19T01:01:24.543875+00:00","updated":"2025-01-19T22:33:32.223676+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00196913","sets":["6504:9795:9801"]},"path":["9801"],"owner":"6748","recid":"196913","title":["音符・コード系列に注目した神経力学モデルによる音楽情報の階層的学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-02-28"},"_buckets":{"deposit":"b6b43296-ccde-4c8d-8ff3-7b392f8bf95a"},"_deposit":{"id":"196913","pid":{"type":"depid","value":"196913","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"音符・コード系列に注目した神経力学モデルによる音楽情報の階層的学習","author_link":["471858","471859","471856","471857"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"音符・コード系列に注目した神経力学モデルによる音楽情報の階層的学習"}]},"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":"2019-02-28","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":"徳島大"},{"subitem_text_value":"徳島大"},{"subitem_text_value":"徳島大"},{"subitem_text_value":"徳島大"}]},"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/196913/files/IPSJ-Z81-4T-04.pdf","label":"IPSJ-Z81-4T-04.pdf"},"date":[{"dateType":"Available","dateValue":"2019-05-28"}],"format":"application/pdf","filename":"IPSJ-Z81-4T-04.pdf","filesize":[{"value":"321.3 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"7a286cb8-9483-4493-9c2b-48e362602bbc","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"天羽, 水穂"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"西出, 俊"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"康, シン"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"任, 福継"}],"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":"本研究の目標は音楽の自動作曲のために神経力学モデルによる音楽情報の階層的な学習である．学習モデルとしてニューロンの発火速度の違いによって情報を階層的に学習することが可能なMTRNNを用いる．学習データは音符系列とコード系列によって構成され，発火速度の速い入力ニューロン群に音符系列を，遅い入力ニューロン群にコード系列を入力してモデルを学習する．単音から成る複数の曲を用いた実験の結果，各楽譜のダイナミクスをパラメータ空間に自己組織化することが可能であることを確認した．自己組織化されたパラメータは曲を表現しており，音符・コード系列とパラメータを相互に計算することが可能であることを確認した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"376","bibliographic_titles":[{"bibliographic_title":"第81回全国大会講演論文集"}],"bibliographicPageStart":"375","bibliographicIssueDates":{"bibliographicIssueDate":"2019-02-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2019"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"id":196913,"links":{}}