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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages698-709
    Number of pages12
    Volume9516
    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)03029743
    ISSN (Electronic)16113349

    Other

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

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    Keywords

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

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Hirai, T., Doi, H., & Morishima, S. (2016). MusicMixer: Automatic DJ system considering beat and latent topic similarity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9516, 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