{"created":"2025-01-19T01:36:27.074181+00:00","updated":"2025-01-19T09:44:52.176713+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00234621","sets":["1164:5064:11558:11626"]},"path":["11626"],"owner":"44499","recid":"234621","title":["機械学習を活用したモジュラーシンセサイザの検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-07"},"_buckets":{"deposit":"0e30a176-4088-4746-9189-ea63e713aa2f"},"_deposit":{"id":"234621","pid":{"type":"depid","value":"234621","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習を活用したモジュラーシンセサイザの検討","author_link":["639549","639547","639548"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を活用したモジュラーシンセサイザの検討"},{"subitem_title":"Integrating Machine Learning Models into Modular Synthesizers","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ポスターセッション1","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-06-07","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京電機大学"},{"subitem_text_value":"東京電機大学"},{"subitem_text_value":"東京電機大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokyo Denki University","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Denki University","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Denki University","subitem_text_language":"en"}]},"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/234621/files/IPSJ-MUS24140009.pdf","label":"IPSJ-MUS24140009.pdf"},"date":[{"dateType":"Available","dateValue":"2026-06-07"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MUS24140009.pdf","filesize":[{"value":"8.3 MB"}],"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":"152da85b-ff82-4de7-9bfd-e8cd75ca0ce8","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"公文, 太一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小坂, 直敏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高橋, 時市郎"}],"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":"ユーロラックモジュラーシンセサイザは異なる機能を持つモジュールを自由に組み合わせることにより音色合成や演奏を行う楽器である.多くのメーカから多様なモジュールが発表されており,パッチケーブルによる自由な接続や動的な演奏性から人気となっている.ここでは,機械学習を活用しモジュラーシンセサイザの演奏を拡張を試み制作した 2 つのモジュールを中心にし議論を行いたい.1 つ目は機械学習による生成機能を持つドラム用シーケンサで,これはユーザが入力したキックのパターンに応じてスネア,ハイハットのパターンを生成するものである.モジュラーシンセサイザの演奏ではアルゴリズムによるパターンの生成が活用されることが多く機械学習を活用したその一例として制作を行った.もう 1 つは機械学習による音合成を行うモジュラーシンセサイザである.リアルタイムに信号合成を行う機械学習モデルである RAVE を活用し,声や自然音といった従来のモジュールでは合成が難しい音色の合成をモジュラーシンセサイザの演奏に導入する.Raspberry Pi を中心にハードウェアを構築しノブや他のモジュールとの接続によって RAVE モデルの潜在空間を探索し音色を合成するモジュールを制作した.これら楽器の評価方法や,楽器と機械学習の組み合わせの活用や発展について議論したい.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"4","bibliographic_titles":[{"bibliographic_title":"研究報告音楽情報科学(MUS)"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-07","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"2024-MUS-140"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":234621,"links":{}}