MusiClef: Multimodal Music Tagging Task

  • Nicola Orio
  • Cynthia C. S. Liem
  • Geoffroy Peeters
  • Markus Schedl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7488)


MusiClef is a multimodal music benchmarking initiative that will be running a MediaEval 2012 Brave New Task on Multimodal Music Tagging. This paper describes the setup of this task, showing how it complements existing benchmarking initiatives and fosters less explored methodological directions in Music Information Retrieval. MusiClef deals with a concrete use case, encourages multimodal approaches based on these, and strives for transparency of results as much as possible. Transparency is encouraged at several levels and stages, from the feature extraction procedure up to the evaluation phase, in which a dedicated categorization of ground truth tags will be used to deepen the understanding of the relation between the proposed approaches and experimental results.


Test Collection Audio Feature Feature Extraction Algorithm Music Information Retrieval Music Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nicola Orio
    • 1
  • Cynthia C. S. Liem
    • 2
  • Geoffroy Peeters
    • 3
  • Markus Schedl
    • 4
  1. 1.University of PaduaItaly
  2. 2.Delft University of TechnologyThe Netherlands
  3. 3.UMR STMS IRCAM-CNRSParisFrance
  4. 4.Johannes Kepler UniversityLinzAustria

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