MusicMixer: Automatic DJ system considering beat and latent topic similarity

Tatsunori Hirai*, Hironori Doi, Shigeo Morishima

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

This paper presents MusicMixer, an automatic DJ system that mixes 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 about how chromatic sounds are generated. Given a list of songs, a DJ selects a song with beat and sounds similar to a specific point of the currently playing song to seamlessly transition between songs. By calculating the similarity of all existing pairs of songs, the proposed system can retrieve the best mixing point from innumerable possibilities. Although it is comparatively easy to calculate beat similarity from audio signals, it has been difficult to consider the semantics of songs as a human DJ considers. To consider such semantics, 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.

本文言語English
ホスト出版物のタイトルMultiMedia Modeling - 22nd International Conference, MMM 2016, Proceedings
編集者Qi Tian, Richang Hong, Xueliang Liu, Nicu Sebe, Benoit Huet, Guo-Jun Qi
出版社Springer Verlag
ページ698-709
ページ数12
ISBN(印刷版)9783319276700
DOI
出版ステータスPublished - 2016
イベント22nd International Conference on MultiMedia Modeling, MMM 2016 - Miami, United States
継続期間: 2016 1月 42016 1月 6

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9516
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other22nd International Conference on MultiMedia Modeling, MMM 2016
国/地域United States
CityMiami
Period16/1/416/1/6

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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