• DocumentCode
    1760620
  • Title

    Guest Editorial: Special Section on Music Data Mining

  • Author

    Li, Tong ; Ogihara, Mitsunori ; Tzanetakis, G.

  • Author_Institution
    School of Computer Science, Florida International University, Miami, FL, USA
  • Volume
    16
  • Issue
    5
  • fYear
    2014
  • fDate
    Aug. 2014
  • Firstpage
    1185
  • Lastpage
    1187
  • Abstract
    The five articles in this special section focus on data mining techniques and applications in the music industry. Music has been an important application area for data mining and machine learning techniques for many years. Music data mining is an interdisciplinary area that studies computational methods for understanding and delivering music data and is a topic of growing importance with large commercial relevance and substantial potential. The research area of music data mining has gradually evolved during this time period in order to address the challenge of effectively accessing and interacting with these increasing large collections of music and associated data such as styles, artists, lyrics and music reviews. The algorithms and systems developed frequently employ sophisticated and advanced data mining and machine learning techniques in their attempt to better capture the frequently elusive relevant music information.
  • Keywords
    Computational modeling; Data mining; Expert systems; Information filtering; Machine learning; Music; Performance evaluation; Recommender systems; Special issues and sections; Unsupervised learning;
  • fLanguage
    English
  • Journal_Title
    Multimedia, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1520-9210
  • Type

    jour

  • DOI
    10.1109/TMM.2014.2325693
  • Filename
    6856270