{"created":"2025-01-19T01:02:19.173430+00:00","updated":"2025-01-19T22:09:40.801142+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00197906","sets":["1164:5159:9712:9831"]},"path":["9831"],"owner":"44499","recid":"197906","title":["屋外拡声品質予測モデルの中間特徴量の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-06-15"},"_buckets":{"deposit":"c0cad661-0752-457a-9da0-5a843d74099b"},"_deposit":{"id":"197906","pid":{"type":"depid","value":"197906","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"屋外拡声品質予測モデルの中間特徴量の検討","author_link":["475691","475692","475694","475693"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"屋外拡声品質予測モデルの中間特徴量の検討"}]},"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":"2019-06-15","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"室蘭工業大学"},{"subitem_text_value":"室蘭工業大学"},{"subitem_text_value":"室蘭工業大学"},{"subitem_text_value":"TOA株式会社"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Muroran Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Muroran Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Muroran Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"TOA Corporation","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/197906/files/IPSJ-SLP19127053.pdf","label":"IPSJ-SLP19127053.pdf"},"date":[{"dateType":"Available","dateValue":"2021-06-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP19127053.pdf","filesize":[{"value":"1.1 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":"22"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"554d7d31-b782-4306-aa43-2de7f8b17b83","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"野口, 啓太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小林, 洋介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"岸上, 順一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"栗栖, 清浩"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10442647","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":"2188-8663","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"東日本大震災では，20% の市民が屋外拡声音をよく聴き取れなかった事が報告されている．このため，屋外拡声器の品質向上が議論されているが，聴取実験のコストがかかるなど解決すべき点は多い．我々はこれまでに，MFCC を音響特徴量として，主観評価指標である LDR  (Listening difficulty rating) を予測する機械学習モデルを提案したが，教師となる主観評価数が少なく，RMSE  (Root means squared error) が 0.20 と満足な性能が得られなかった．そこで本稿では，MFCC から中間特徴量となる客観評価値を予測するモデルと，中間特徴量から主観評価値を予測する 2 モデルの組み合わせを提案する．中間特徴量に用いる指標に，Short time objective intelligibility，Speech intelligibility prediction based on mutual information，Extended short time objective intelligibility の 3 指標を比較した．その結果，最適な中間特徴量は SNR により異なったものの，SNR が0 dB から 30 dB の範囲では以前の検討よりも良い，RMSE が 0.14 以下を達成した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"4","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-06-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"53","bibliographicVolumeNumber":"2019-SLP-127"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":197906,"links":{}}