Latent Topic Similarity for Music Retrieval and Its Application to a System that Supports DJ Performance

• Item type: Journal(1)
• 公開日: 2018-03-15
• タイトル: Latent Topic Similarity for Music Retrieval and Its Application to a System that Supports DJ Performance
• タイトル / 言語: en
• タイトル: Latent Topic Similarity for Music Retrieval and Its Application to a System that Supports DJ Performance
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：若手研究者] automatic song mixing, topic analysis, computer-aided performance
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Faculty of Global Media Studies, Komazawa University
• 著者所属: Dwango
• 著者所属: Waseda Research Institute for Science and Engineering
• 著者所属(英): en
• 著者所属(英): Faculty of Global Media Studies, Komazawa University
• 著者所属(英): en
• 著者所属(英): Dwango
• 著者所属(英): en
• 著者所属(英): Waseda Research Institute for Science and Engineering
• 著者名: Tatsunori, Hirai; Hironori, Doi; Shigeo, Morishima
• 著者名(英): Tatsunori, Hirai; Hironori, Doi; Shigeo, Morishima
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.26(2018) (online); DOI http://dx.doi.org/10.2197/ipsjjip.26.276; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.26(2018) (online); DOI http://dx.doi.org/10.2197/ipsjjip.26.276; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 59, 号 3, 発行日 2018-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764