{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00055849","sets":["1164:5064:5077:5080"]},"path":["5080"],"owner":"1","recid":"55849","title":["SOMを用いたベースラインからの音楽ジャンル解析"],"pubdate":{"attribute_name":"公開日","attribute_value":"2006-08-07"},"_buckets":{"deposit":"1c0b3f71-205f-46aa-a650-35b68db9cfdb"},"_deposit":{"id":"55849","pid":{"type":"depid","value":"55849","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"SOMを用いたベースラインからの音楽ジャンル解析","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"SOMを用いたベースラインからの音楽ジャンル解析"},{"subitem_title":"Music Genre Classification from Base-Part using SOM","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2006-08-07","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"関西学院大学理工学部研究科"},{"subitem_text_value":"関西学院大学理工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kwansei Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Kwansei Gakuin University","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/55849/files/IPSJ-MUS06066006.pdf"},"date":[{"dateType":"Available","dateValue":"2008-08-07"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MUS06066006.pdf","filesize":[{"value":"574.5 kB"}],"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":"1bfb2d77-bc38-4b15-8832-c855062fbc86","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2006 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"土橋, 佑亮"},{"creatorName":"片寄, 晴弘"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"YUSUKE, TSUCHIHASHI","creatorNameLang":"en"},{"creatorName":"HARUHIRO, KATAYOSE","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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"音楽ジャンルはWeb上での楽曲検索において有力な指標となる.これまで音響信号を用いての様々な音楽ジャンル解析の研究がなされてきたが そのほとんどは様々なパートが混成する音楽を対象していた.本稿では複音からの音源分離が比較的容易なベースパートに注目したジャンル推定を取り扱う.まずスケール リズム 音色の等の特徴量の設定と有効性の考察をし それらを用いてマハラノビス距離 F値最大境界による実験を行う.更にMusic Islandを利用し ジャンルの可視化と島の変化を調べる.マハラノビス距離による音楽ジャンル解析において Metal/Punkでは73％ Jazz/Bluesでは80％の認識率を得た.Music Islandにおいては 注目する特徴量に応じて島が変化することを確認した.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Musical genre helps us to search for songs on the web. Most of the previous works have focused on audio signal analysis for the pieces composing of various instruments. This paper presents an approach to music genre classification focusing on base part,the fundamental frequency of which is comparatively easy to be estimated. First, the paper describes features regarding scale, rhythm, timber, and examine those validity. Next, this paper describes about twe experiments based on maharanobis distance and  one song to multi-genre correspondence. Finally, we illustrated music genre visualization with Music Island based on SOM. Experimental results by using mahalanobis distance show success rates of 78％ for Metal/Punk, and 80％ for Jazz/Blues.  And we confirmed  transformation of Music Island depending on each features.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"36","bibliographic_titles":[{"bibliographic_title":"情報処理学会研究報告音楽情報科学（MUS）"}],"bibliographicPageStart":"31","bibliographicIssueDates":{"bibliographicIssueDate":"2006-08-07","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"90(2006-MUS-066)","bibliographicVolumeNumber":"2006"}]},"relation_version_is_last":true,"weko_creator_id":"1"},"created":"2025-01-18T23:19:24.138918+00:00","updated":"2025-01-22T05:15:12.850472+00:00","id":55849}