{"created":"2025-01-19T01:29:31.056842+00:00","updated":"2025-01-19T11:19:32.910231+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230012","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230012","title":["音源定位・分離の同時学習に基づく移動音源の深層ブラインド音源分離"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"b99848da-1bde-4001-878e-86342de19932"},"_deposit":{"id":"230012","pid":{"type":"depid","value":"230012","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"音源定位・分離の同時学習に基づく移動音源の深層ブラインド音源分離","author_link":["618808","618809","618811","618807","618810"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"音源定位・分離の同時学習に基づく移動音源の深層ブラインド音源分離"}]},"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":"阪大"},{"subitem_text_value":"産総研"},{"subitem_text_value":"阪大"},{"subitem_text_value":"阪大"},{"subitem_text_value":"産総研"}]},"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/230012/files/IPSJ-Z85-5S-01.pdf","label":"IPSJ-Z85-5S-01.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-5S-01.pdf","filesize":[{"value":"438.2 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"3db1098e-21d1-493f-99e0-6184ce9b59e4","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":"宗像, 北斗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"坂東, 宜昭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"武田, 龍"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"駒谷, 和範"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大西, 正輝"}],"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":"音源分離は深層学習の発達に伴い高い性能を達成しているが,日常生活の様々な音源を分離するにはいまだ課題がある.教師なしで学習可能な深層フルランク空間相関分析は高い分離性能が報告されており有望だが,音源がほとんど動かないと仮定している.本稿では深層フルランク空間相関分析を拡張し,移動音源の分離と定位を可能にする.具体的にはまず,時変な空間相関行列と音源の到来方向に基づく多チャンネル混合信号の生成過程を導入する.この生成過程に基づき,混合信号とマイクロホン配置のみから周辺事後確率を最大化するように定位,分離モデルを同時に学習する.実験の結果,提案法の分離精度は従来法と比較して大きく改善した.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"434","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"433","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230012,"links":{}}