• DocumentCode
    104066
  • Title

    Revisiting Inter-Genre Similarity

  • Author

    Sturm, Bob L. ; Gouyon, Fabien

  • Author_Institution
    Audio Anal. Lab., Aalborg Univ. Copenhagen, Copenahgen, Denmark
  • Volume
    20
  • Issue
    11
  • fYear
    2013
  • fDate
    Nov. 2013
  • Firstpage
    1050
  • Lastpage
    1053
  • Abstract
    We revisit the idea of “inter-genre similarity” (IGS) for machine learning in general, and music genre recognition in particular. We show analytically that the probability of error for IGS is higher than naive Bayes classification with zero-one loss (NB). We show empirically that IGS does not perform well, even for data that satisfies all its assumptions.
  • Keywords
    Bayes methods; error statistics; learning (artificial intelligence); music; pattern classification; IGS; error probability; intergenre similarity; machine learning; music genre recognition; naive Bayes classification; zero-one loss; Abstracts; Indexes; Materials; Niobium; Pattern recognition; Training; Vectors; Content-based processing and music information retrieval; pattern recognition and classification;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2013.2280031
  • Filename
    6587787
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=104066