{"id":232500,"links":{},"created":"2025-01-19T01:33:24.269919+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00232500","sets":["1164:5159:11541:11549"]},"path":["11549"],"owner":"44499","recid":"232500","title":["LSTM 特徴抽出と楽譜分析による特定のピアニストの演奏スタイルを模倣する自動演奏に関する研究"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-02-22"},"_buckets":{"deposit":"68c9b9a0-e21a-41f2-9f57-b1f0397aa1d4"},"_deposit":{"id":"232500","pid":{"type":"depid","value":"232500","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"LSTM 特徴抽出と楽譜分析による特定のピアニストの演奏スタイルを模倣する自動演奏に関する研究","author_link":["629417","629418","629415","629416"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"LSTM 特徴抽出と楽譜分析による特定のピアニストの演奏スタイルを模倣する自動演奏に関する研究"},{"subitem_title":"A Study on Automatic Performance for Emulating the Playing Style of a Specific Pianist using Feature Extraction with LSTM and Score Analysis","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"SP2","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-02-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"日本大学大学院理工学研究科情報科学専攻"},{"subitem_text_value":"日本大学大学院理工学研究科情報科学専攻"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Computer Engineering, College of Science and Technology, Nihon University","subitem_text_language":"en"},{"subitem_text_value":"Department of Computer Engineering, College of Science and Technology, Nihon University","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/232500/files/IPSJ-SLP24151030.pdf","label":"IPSJ-SLP24151030.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP24151030.pdf","filesize":[{"value":"2.7 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"22"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"d440857d-7412-43d5-95fb-6396fb1280b0","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"李, 森浩"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松野, 裕"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Senhao, Li","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yutaka, Matsuno","creatorNameLang":"en"}],"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":"Classical music continues to captivate audiences worldwide, and advancements in automatic piano playing technologies and the Internet allows people to enjoy professional piano performances at home. However, the labor-intensive process of recording tracks on sensor-equipped pianos and the limited repertoire available present challenges. Thus, in this paper, we propose a neural network–based method to generate automatic piano performances. The proposed method utilizes long short-term memory (LSTM) networks, designed for the specific pianist, and contributes to the field of music notation learning. We employ LSTM networks rather than a recurrent neural network to decipher and encapsulate the intricate temporal dependencies characteristic of piano performances. We conducted training loss evaluation and pianist perception assessment. Experimental outcomes demonstrated predictive accuracy with R-squared (R2) values between 0.4 and 0.6. Blind listening tests revealed the model’s effectiveness in generating performances that are comparable to those of skilled pianists.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Classical music continues to captivate audiences worldwide, and advancements in automatic piano playing technologies and the Internet allows people to enjoy professional piano performances at home. However, the labor-intensive process of recording tracks on sensor-equipped pianos and the limited repertoire available present challenges. Thus, in this paper, we propose a neural network–based method to generate automatic piano performances. The proposed method utilizes long short-term memory (LSTM) networks, designed for the specific pianist, and contributes to the field of music notation learning. We employ LSTM networks rather than a recurrent neural network to decipher and encapsulate the intricate temporal dependencies characteristic of piano performances. We conducted training loss evaluation and pianist perception assessment. Experimental outcomes demonstrated predictive accuracy with R-squared (R2) values between 0.4 and 0.6. Blind listening tests revealed the model’s effectiveness in generating performances that are comparable to those of skilled pianists.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-02-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"30","bibliographicVolumeNumber":"2024-SLP-151"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T10:25:38.559208+00:00"}