{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00236214","sets":["6504:11678:11689"]},"path":["11689"],"owner":"44499","recid":"236214","title":["自己教師あり音高推定手法PICIEにおけるキャリブレーションの改善"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"db51d3b7-540f-43ea-bc67-e4b4bd7a5c3f"},"_deposit":{"id":"236214","pid":{"type":"depid","value":"236214","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"自己教師あり音高推定手法PICIEにおけるキャリブレーションの改善","author_link":["645646","645647","645648"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"自己教師あり音高推定手法PICIEにおけるキャリブレーションの改善"}]},"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":"2024-03-01","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"}]},"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/236214/files/IPSJ-Z86-1V-03.pdf","label":"IPSJ-Z86-1V-03.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-03"}],"format":"application/pdf","filename":"IPSJ-Z86-1V-03.pdf","filesize":[{"value":"506.1 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"c4e98a18-eda5-4ac6-b048-664303e217cf","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"石江, 悠眞"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大野, 将樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"獅々堀, 正幹"}],"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":"SPICE（Self-Supervised Pitch Estimation）は音楽音響信号の音高（基本周波数）を推定する自己教師あり学習手法であり，2つの音楽音響信号の音高差を推定するモデルを訓練することで，間接的に音高を推定する．SPICEのキャリブレーション処理は，周波数が自明な合成音をSPICEの学習済みモデルに入力して得た音高の推定値を説明変数，合成音の周波数を目的変数として線形回帰し，音高の推定値を周波数に変換する．しかし，合成音と実際の音声は調波構造が異なるため，最適なキャリブレーションが実現できない．本研究では，少量の教師データを用いることでキャリブレーションを最適化する．歌唱データ1,000曲に対する実験により，提案法の音高推定精度が向上することを確かめた.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"762","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"761","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":236214,"updated":"2025-01-19T09:20:55.742084+00:00","links":{},"created":"2025-01-19T01:38:08.584851+00:00"}