Live version identification with audio scene detection

Kazumasa Ishikura, Aiko Uemura, Jiro Katto

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

    Abstract

    This paper presents a live version music identification system by modifying the conventional cover song identification system. The proposed system includes two stages: a live version identification phase and an audio scenedetection phase. We improve the accuracy of the system by weighting similarity scores in the live version identification phase and discriminating scenes by using RMS, pulse clarity and similarity scores. Results show that the proposed method performs better than the previous method. The final algorithm achieves 70% accuracy on average.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages408-417
    Number of pages10
    Volume8935
    ISBN (Print)9783319144443
    Publication statusPublished - 2015
    Event21st International Conference on MultiMedia Modeling, MMM 2015 - Sydney
    Duration: 2015 Jan 52015 Jan 7

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume8935
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other21st International Conference on MultiMedia Modeling, MMM 2015
    CitySydney
    Period15/1/515/1/7

    Fingerprint

    Identification (control systems)
    System Identification
    Music
    Weighting
    Cover
    Similarity

    Keywords

    • Audio scene detection
    • Live version
    • Music identification

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Ishikura, K., Uemura, A., & Katto, J. (2015). Live version identification with audio scene detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8935, pp. 408-417). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8935). Springer Verlag.

    Live version identification with audio scene detection. / Ishikura, Kazumasa; Uemura, Aiko; Katto, Jiro.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8935 Springer Verlag, 2015. p. 408-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8935).

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

    Ishikura, K, Uemura, A & Katto, J 2015, Live version identification with audio scene detection. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8935, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8935, Springer Verlag, pp. 408-417, 21st International Conference on MultiMedia Modeling, MMM 2015, Sydney, 15/1/5.
    Ishikura K, Uemura A, Katto J. Live version identification with audio scene detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8935. Springer Verlag. 2015. p. 408-417. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
    Ishikura, Kazumasa ; Uemura, Aiko ; Katto, Jiro. / Live version identification with audio scene detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8935 Springer Verlag, 2015. pp. 408-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
    @inproceedings{f80693bc5bdf4cc5a4e8336855743789,
    title = "Live version identification with audio scene detection",
    abstract = "This paper presents a live version music identification system by modifying the conventional cover song identification system. The proposed system includes two stages: a live version identification phase and an audio scenedetection phase. We improve the accuracy of the system by weighting similarity scores in the live version identification phase and discriminating scenes by using RMS, pulse clarity and similarity scores. Results show that the proposed method performs better than the previous method. The final algorithm achieves 70{\%} accuracy on average.",
    keywords = "Audio scene detection, Live version, Music identification",
    author = "Kazumasa Ishikura and Aiko Uemura and Jiro Katto",
    year = "2015",
    language = "English",
    isbn = "9783319144443",
    volume = "8935",
    series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    publisher = "Springer Verlag",
    pages = "408--417",
    booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

    }

    TY - GEN

    T1 - Live version identification with audio scene detection

    AU - Ishikura, Kazumasa

    AU - Uemura, Aiko

    AU - Katto, Jiro

    PY - 2015

    Y1 - 2015

    N2 - This paper presents a live version music identification system by modifying the conventional cover song identification system. The proposed system includes two stages: a live version identification phase and an audio scenedetection phase. We improve the accuracy of the system by weighting similarity scores in the live version identification phase and discriminating scenes by using RMS, pulse clarity and similarity scores. Results show that the proposed method performs better than the previous method. The final algorithm achieves 70% accuracy on average.

    AB - This paper presents a live version music identification system by modifying the conventional cover song identification system. The proposed system includes two stages: a live version identification phase and an audio scenedetection phase. We improve the accuracy of the system by weighting similarity scores in the live version identification phase and discriminating scenes by using RMS, pulse clarity and similarity scores. Results show that the proposed method performs better than the previous method. The final algorithm achieves 70% accuracy on average.

    KW - Audio scene detection

    KW - Live version

    KW - Music identification

    UR - http://www.scopus.com/inward/record.url?scp=84919683746&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84919683746&partnerID=8YFLogxK

    M3 - Conference contribution

    AN - SCOPUS:84919683746

    SN - 9783319144443

    VL - 8935

    T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    SP - 408

    EP - 417

    BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    PB - Springer Verlag

    ER -