{"created":"2025-01-19T01:36:31.716572+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00234669","sets":["1164:5064:11558:11626"]},"path":["11626"],"owner":"44499","recid":"234669","title":["Distilling the Class-to-class Distances Encoded in a Classifier to Accelerate DTW of its Posteriorgram"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-07"},"_buckets":{"deposit":"941306c3-8173-4ae2-8a8f-87037978e4e4"},"_deposit":{"id":"234669","pid":{"type":"depid","value":"234669","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Distilling the Class-to-class Distances Encoded in a Classifier to Accelerate DTW of its Posteriorgram","author_link":["639754","639750","639749","639747","639753","639751","639752","639748"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Distilling the Class-to-class Distances Encoded in a Classifier to Accelerate DTW of its Posteriorgram"},{"subitem_title":"Distilling the Class-to-class Distances Encoded in a Classifier to Accelerate DTW of its Posteriorgram","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2","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":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"},{"subitem_text_value":"The University of Tokyo"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/234669/files/IPSJ-MUS24140057.pdf","label":"IPSJ-MUS24140057.pdf"},"date":[{"dateType":"Available","dateValue":"2026-06-07"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MUS24140057.pdf","filesize":[{"value":"5.3 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":"1af5fdfd-2b51-4452-a5b2-a06d137d4f38","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":"Haitong, Sun"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jaehyun, Choi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuaki, Minematsu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daisuke, Saito"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Haitong, Sun","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jaehyun, Choi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobuaki, Minematsu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daisuke, Saito","creatorNameLang":"en"}],"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":"In media technology, comparison of a sequential data with another is a fundamental technique for many applications, and dynamic time warping is often conducted. Conventionally, two sequences of raw features were compared. These days, more abstract and sequential representations are used, which are obtained with deep neural networks. One of these representations is posteriorgram, where each frame is composed of n-dimensional class posteriors, and frame-to-frame distance is often calculated using a divergence metric such as Bhattacharyya distance. In this study, a novel method is proposed to distill the class-to-class distances encoded in any classifier used to calculate posteriors, and the distances are effectively used to accelerate posteriorgram-based DTW by approximating it as DTW between two sequences of most likely classes. Utterance comparison experiments showed that the proposed method can accelerate the distance calculation step in posteriorgram-based DTW by a factor of 30.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In media technology, comparison of a sequential data with another is a fundamental technique for many applications, and dynamic time warping is often conducted. Conventionally, two sequences of raw features were compared. These days, more abstract and sequential representations are used, which are obtained with deep neural networks. One of these representations is posteriorgram, where each frame is composed of n-dimensional class posteriors, and frame-to-frame distance is often calculated using a divergence metric such as Bhattacharyya distance. In this study, a novel method is proposed to distill the class-to-class distances encoded in any classifier used to calculate posteriors, and the distances are effectively used to accelerate posteriorgram-based DTW by approximating it as DTW between two sequences of most likely classes. Utterance comparison experiments showed that the proposed method can accelerate the distance calculation step in posteriorgram-based DTW by a factor of 30.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","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":"57","bibliographicVolumeNumber":"2024-MUS-140"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":234669,"updated":"2025-01-19T09:43:54.785262+00:00","links":{}}