{"updated":"2025-01-20T02:26:50.843878+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00186827","sets":["581:9322:9325"]},"path":["9325"],"owner":"11","recid":"186827","title":["Latent Topic Similarity for Music Retrieval and Its Application to a System that Supports DJ Performance"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-03-15"},"_buckets":{"deposit":"c1a0018a-a0bc-43b0-8278-1bdd682cad09"},"_deposit":{"id":"186827","pid":{"type":"depid","value":"186827","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Latent Topic Similarity for Music Retrieval and Its Application to a System that Supports DJ Performance","author_link":["420332","420331","420330","420329","420328","420333"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Latent Topic Similarity for Music Retrieval and Its Application to a System that Supports DJ Performance"},{"subitem_title":"Latent Topic Similarity for Music Retrieval and Its Application to a System that Supports DJ Performance","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集：若手研究者] automatic song mixing, topic analysis, computer-aided performance","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2018-03-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Faculty of Global Media Studies, Komazawa University"},{"subitem_text_value":"Dwango"},{"subitem_text_value":"Waseda Research Institute for Science and Engineering"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Global Media Studies, Komazawa University","subitem_text_language":"en"},{"subitem_text_value":"Dwango","subitem_text_language":"en"},{"subitem_text_value":"Waseda Research Institute for Science and Engineering","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/186827/files/IPSJ-JNL5903018.pdf","label":"IPSJ-JNL5903018.pdf"},"date":[{"dateType":"Available","dateValue":"2020-03-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL5903018.pdf","filesize":[{"value":"2.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"783b3008-3004-48e6-a6cc-b604b911187f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tatsunori, Hirai"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hironori, Doi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shigeo, Morishima"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tatsunori, Hirai","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hironori, Doi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shigeo, Morishima","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","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_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"This paper presents a topic modeling method to retrieve similar music fragments and its application, Music-Mixer, which is a computer-aided DJ system that supports DJ performance by automatically mixing songs in a seamless manner. MusicMixer mixes songs based on audio similarity calculated via beat analysis and latent topic analysis of the chromatic signal in the audio. The topic represents latent semantics on how chromatic sounds are generated. Given a list of songs, a DJ selects a song with beats and sounds similar to a specific point of the currently playing song to seamlessly transition between songs. By calculating similarities between all existing song sections that can be naturally mixed, MusicMixer retrieves the best mixing point from a myriad of possibilities and enables seamless song transitions. Although it is comparatively easy to calculate beat similarity from audio signals, considering the semantics of songs from the viewpoint of a human DJ has proven difficult. Therefore, we propose a method to represent audio signals to construct topic models that acquire latent semantics of audio. The results of a subjective experiment demonstrate the effectiveness of the proposed latent semantic analysis method. MusicMixer achieves automatic song mixing using the audio signal processing approach; thus, users can perform DJ mixing simply by selecting a song from a list of songs suggested by the system.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.26(2018) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.26.276\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper presents a topic modeling method to retrieve similar music fragments and its application, Music-Mixer, which is a computer-aided DJ system that supports DJ performance by automatically mixing songs in a seamless manner. MusicMixer mixes songs based on audio similarity calculated via beat analysis and latent topic analysis of the chromatic signal in the audio. The topic represents latent semantics on how chromatic sounds are generated. Given a list of songs, a DJ selects a song with beats and sounds similar to a specific point of the currently playing song to seamlessly transition between songs. By calculating similarities between all existing song sections that can be naturally mixed, MusicMixer retrieves the best mixing point from a myriad of possibilities and enables seamless song transitions. Although it is comparatively easy to calculate beat similarity from audio signals, considering the semantics of songs from the viewpoint of a human DJ has proven difficult. Therefore, we propose a method to represent audio signals to construct topic models that acquire latent semantics of audio. The results of a subjective experiment demonstrate the effectiveness of the proposed latent semantic analysis method. MusicMixer achieves automatic song mixing using the audio signal processing approach; thus, users can perform DJ mixing simply by selecting a song from a list of songs suggested by the system.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.26(2018) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.26.276\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2018-03-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"59"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:53:45.130424+00:00","id":186827,"links":{}}