A Learning-Based Model for Musical Data Representation Using Histograms

  • Mehdi Naccache
  • Amel Borgi
  • Khaled Ghédira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5493)


In this paper we are interested in musical data classification. For musical features representation, we propose to adopt a histogram structure in order to preserve a maximum amount of information. The melodic dimension of the data is described in terms of pitch values, pitch intervals, melodic direction and durations of notes as well as silences. Our purpose is to have a data representation well suited to a generic framework for classifying melodies by means of known supervised Machine Learning (ML) algorithms. Since such algorithms are not expected to handle histogram-based feature values, we propose to transform the representation space in the pattern recognition process. This transformation is realized by partitioning the domain of each attribute using a clustering technique. The model is evaluated experimentally by implementing three kinds of classifiers (musical genre, composition style and emotional content).


Representation Space Emotional Content Music Information Retrieval Pitch Interval Musical 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 2009

Authors and Affiliations

  • Mehdi Naccache
    • 1
  • Amel Borgi
    • 2
  • Khaled Ghédira
    • 1
  1. 1.ENSI, Campus Universitaire ManoubaManoubaTunisia
  2. 2.INSAT, Centre Urbain Nord de TunisTunisie

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