{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00095672","sets":["5471:7004:7294"]},"path":["7294"],"owner":"11","recid":"95672","title":["Robust Multipitch Analyzer against Initialization based on Latent Harmonic Allocation using Overtone Corpus"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-04-15"},"_buckets":{"deposit":"571dd18a-287c-407f-ae95-631cfaf9bda1"},"_deposit":{"id":"95672","pid":{"type":"depid","value":"95672","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Robust Multipitch Analyzer against Initialization based on Latent Harmonic Allocation using Overtone Corpus","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Robust Multipitch Analyzer against Initialization based on Latent Harmonic Allocation using Overtone Corpus"},{"subitem_title":"Robust Multipitch Analyzer against Initialization based on Latent Harmonic Allocation using Overtone Corpus","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[Special Issue on New Directions in Music Information Processing (Special Issue Marking the 20th Anniversary of IPSJ SIGMUS)] multipitch estimation, harmonic clustering, overtone estimation, musical instrument sounds","subitem_subject_scheme":"Other"}]},"item_type_id":"5","publish_date":"2013-04-15","item_5_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Informatics, Kyoto University"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University／Currently, Faculty of Science and Engineering, Waseda University"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University"}]},"item_5_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Informatics, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Kyoto University / Currently, Faculty of Science and Engineering, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Kyoto 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/95672/files/IPSJ-JIP2102013.pdf"},"date":[{"dateType":"Available","dateValue":"2015-04-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JIP2102013.pdf","filesize":[{"value":"603.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"11f7f6b3-4135-4858-b258-88a8452eb5ec","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2013 by the Information Processing Society of Japan"}]},"item_5_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Daichi, Sakaue"},{"creatorName":"Katsutoshi, Itoyama"},{"creatorName":"Tetsuya, Ogata"},{"creatorName":"HiroshiG.Okuno"}],"nameIdentifiers":[{}]}]},"item_5_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Daichi, Sakaue","creatorNameLang":"en"},{"creatorName":"Katsutoshi, Itoyama","creatorNameLang":"en"},{"creatorName":"Tetsuya, Ogata","creatorNameLang":"en"},{"creatorName":"Hiroshi, G.Okuno","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_5_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA00700121","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_5_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-6652","subitem_source_identifier_type":"ISSN"}]},"item_5_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"We present a Bayesian analysis method that estimates the harmonic structure of musical instruments in music signals on the basis of psychoacoustic evidence. Since the main objective of multipitch analysis is joint estimation of the fundamental frequencies and their harmonic structures, the performance of harmonic structure estimation significantly affects fundamental frequency estimation accuracy. Many methods have been proposed for estimating the harmonic structure accurately, but no method has been proposed that satisfies all these requirements: robust against initialization, optimization-free, and psychoacoustically appropriate and thus easy to develop further. Our method satisfies these requirements by explicitly incorporating Terhardt's virtual pitch theory within a Bayesian framework. It does this by automatically learning the valid weight range of the harmonic components using a MIDI synthesizer. The bounds are termed “overtone corpus.” Modeling demonstrated that the proposed overtone corpus method can stably estimate the harmonic structure of 40 musical pieces for a wide variety of initial settings.","subitem_description_type":"Other"}]},"item_5_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"We present a Bayesian analysis method that estimates the harmonic structure of musical instruments in music signals on the basis of psychoacoustic evidence. Since the main objective of multipitch analysis is joint estimation of the fundamental frequencies and their harmonic structures, the performance of harmonic structure estimation significantly affects fundamental frequency estimation accuracy. Many methods have been proposed for estimating the harmonic structure accurately, but no method has been proposed that satisfies all these requirements: robust against initialization, optimization-free, and psychoacoustically appropriate and thus easy to develop further. Our method satisfies these requirements by explicitly incorporating Terhardt's virtual pitch theory within a Bayesian framework. It does this by automatically learning the valid weight range of the harmonic components using a MIDI synthesizer. The bounds are termed “overtone corpus.” Modeling demonstrated that the proposed overtone corpus method can stably estimate the harmonic structure of 40 musical pieces for a wide variety of initial settings.","subitem_description_type":"Other"}]},"item_5_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"255","bibliographic_titles":[{"bibliographic_title":"Journal of information processing"}],"bibliographicPageStart":"246","bibliographicIssueDates":{"bibliographicIssueDate":"2013-04-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"21"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":95672,"updated":"2025-01-21T13:38:59.191677+00:00","links":{},"created":"2025-01-18T23:42:41.608711+00:00"}