Using Psycho-Acoustic Models and Self-Organizing Maps to Create a 
         Hierarchical Structuring of Music by Musical Styles

       Andreas Rauber (1), Elias Pampalk (2), Dieter Merkl (1)

           (1) Dept. of Software Technology
               Vienna Univ of Technology
               A-1040 Vienna, Austria
               {andi, dieter}@ifs.tuwien.ac.at

           (2) Austrian Research Institute for Artificial Intelligence
               A-1010 Vienna, Austria
               elias@ai.univie.ac.at


Abstract:

With the advent of large musical archives the need to provide an
organization of these archives becomes eminent. While artist-based
organizations or title indexes may help in locating a specific piece of
music, a more intuitive, genre-based organization is required to allow
users to browse an archive and explore its contents. Yet, currently these
organizations following musical styles have to be designed manually.

In this paper we propose an approach to automatically create a hierarchical
organization of music archives following their perceived sound similarity.
More specifically, characteristics of frequency spectra are extracted and
transformed according to psycho-acoustic models. Subsequently, the Growing
Hierarchical Self-Organizing Map, a popular unsupervised neural network, is
used to create a hierarchical organization, offering both an interface for
interactive exploration as well as retrieval of music according to sound
similarity.

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Proceedings of the 3rd International Symposium on Music Information 
Retrieval (ISMIR 2002), pp. 71-80, October 13-17, 2002, Paris, France.
http://www.ifs.tuwien.ac.at/ifs/research/publications.html