{"created":"2025-01-19T01:29:31.152603+00:00","updated":"2025-01-19T11:19:31.500453+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230013","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230013","title":["深層ブラインド音源分離を用いた転移学習による環境音分離"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"90448d08-8da5-45a8-86a1-2a41331d4c1e"},"_deposit":{"id":"230013","pid":{"type":"depid","value":"230013","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"深層ブラインド音源分離を用いた転移学習による環境音分離","author_link":["618812","618816","618813","618815","618814"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層ブラインド音源分離を用いた転移学習による環境音分離"}]},"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":"東京工業大/産業技術総合研究所"},{"subitem_text_value":"産総研"},{"subitem_text_value":"東京工業大/HRI-JP"},{"subitem_text_value":"東工大"},{"subitem_text_value":"東工大"}]},"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/230013/files/IPSJ-Z85-5S-02.pdf","label":"IPSJ-Z85-5S-02.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-5S-02.pdf","filesize":[{"value":"380.6 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"0e5e703d-91a3-486f-8f2e-a840f2efb322","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":"合澤, 隆拓"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"坂東, 宜昭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"糸山, 克寿"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"西田, 健次"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中臺, 一博"}],"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":"音環境理解において雑踏環境下での音源分離は，環境音認識の基盤技術の一つである．環境音は音声と異なり，スペクトル構造が多様であり，事前にあらゆる環境に適応できるモデルを学習することが難しい．本研究では，非線形ブラインド音源分離法のひとつである深層フルランク空間相関分析法を用いた目的環境への教師なし転移学習を行う．事前学習データに含まれない未知の音源信号が混合音に含まれていても，多チャネル信号の空間情報を表す周辺尤度関数に基づき，混合音のみから分離モデルを改善するよう学習できる．環境音を用いた数値混合音により，推論データに対する教師なし転移学習の有効性を確認した．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"436","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"435","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230013,"links":{}}