MusicMixer: Automatic DJ system considering beat and latent topic similarity

Tatsunori Hirai, Hironori Doi, Shigeo Morishima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 22nd International Conference, MMM 2016, Proceedings
EditorsQi Tian, Richang Hong, Xueliang Liu, Nicu Sebe, Benoit Huet, Guo-Jun Qi
PublisherSpringer Verlag
Pages698-709
Number of pages12
ISBN (Print)9783319276700
DOIs
Publication statusPublished - 2016
Event22nd International Conference on MultiMedia Modeling, MMM 2016 - Miami, United States
Duration: 2016 Jan 42016 Jan 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9516
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

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

Keywords

  • Beat similarity
  • DJ system
  • Latent topic analysis
  • Machine learning
  • Song mixing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Hirai, T., Doi, H., & Morishima, S. (2016). MusicMixer: Automatic DJ system considering beat and latent topic similarity. In Q. Tian, R. Hong, X. Liu, N. Sebe, B. Huet, & G-J. Qi (Eds.), MultiMedia Modeling - 22nd International Conference, MMM 2016, Proceedings (pp. 698-709). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9516). Springer Verlag. https://doi.org/10.1007/978-3-319-27671-7_59