{"id":197006,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00197006","sets":["6504:9795:9801"]},"path":["9801"],"owner":"6748","recid":"197006","title":["中咽頭部収録音とLSTM-CTCを用いた咀嚼回数の自動推定"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-02-28"},"_buckets":{"deposit":"3b7642f4-9f05-4801-8666-103e35bcf28f"},"_deposit":{"id":"197006","pid":{"type":"depid","value":"197006","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"中咽頭部収録音とLSTM-CTCを用いた咀嚼回数の自動推定","author_link":["472133","472135","472134","472137","472136"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"中咽頭部収録音とLSTM-CTCを用いた咀嚼回数の自動推定"}]},"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":"静岡大"},{"subitem_text_value":"NTTドコモ"},{"subitem_text_value":"NTTドコモ"},{"subitem_text_value":"静岡大"},{"subitem_text_value":"静岡大"}]},"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/197006/files/IPSJ-Z81-4V-08.pdf","label":"IPSJ-Z81-4V-08.pdf"},"date":[{"dateType":"Available","dateValue":"2019-05-28"}],"format":"application/pdf","filename":"IPSJ-Z81-4V-08.pdf","filesize":[{"value":"562.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"53b5de7d-325b-486a-a566-8f3dbe091537","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":"阿部, 太樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"齊藤, 隆仁"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"池田, 大造"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"峰野, 博史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"西村, 雅史"}],"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":"健康維持の観点から，咀嚼から嚥下に至る一連の食事行動を，中咽頭付近で収録した音情報を用いて簡便かつ詳細にモニタリングできるシステムの検討を行っている．今回特に咀嚼音に着目し，その回数の自動推定方法について検討した．通常のDNNを用いる場合，学習データに対して正確な時間情報ラベルが必要となるが，ここではConnectionist Temporal Classification(CTC)を損失関数とするLSTMを用いることで，時間情報ラベルのないデータで学習を行う．実験の結果，限られた量の時間情報ラベル付きデータで学習されたLSTMと比較して，咀嚼回数の推定精度が改善される見通しを得た．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"566","bibliographic_titles":[{"bibliographic_title":"第81回全国大会講演論文集"}],"bibliographicPageStart":"565","bibliographicIssueDates":{"bibliographicIssueDate":"2019-02-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2019"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"updated":"2025-01-19T22:35:50.905518+00:00","created":"2025-01-19T01:01:29.790851+00:00","links":{}}