Searching for better measures: Generating similarity functions for abstract musical objects

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Atte Tenkanen",
  title =        "Searching for better measures: Generating similarity
                 functions for abstract musical objects",
  booktitle =    "IEEE International Conference on Intelligent Computing
                 and Intelligent Systems, ICIS 2009",
  year =         "2009",
  month =        nov,
  volume =       "4",
  pages =        "472--476",
  keywords =     "genetic algorithms, genetic programming, abstract
                 musical objects, distance measures, music information
                 retrieval, pitch-class set theory, similarity
                 functions, information retrieval, music, set theory",
  DOI =          "doi:10.1109/ICICISYS.2009.5357625",
  abstract =     "Several similarity and distance measures have been
                 developed for different purposes and applications in
                 various research fields. For example, scholars have
                 used them to evaluate similarities between tonalities,
                 melodies and rhythms for music information retrieval.
                 In this study, similarity functions are generated
                 automatically. We focus on similarities between the
                 so-called pitch-class sets that belong to the field of
                 pitch-class set theory. Pitch-class set theory offers a
                 well-defined mathematical framework for categorising
                 musical objects and describing their relationships. An
                 output, consisting of similarity values between the
                 abstract pitch-class sets, is produced by means of a
                 generated function. We then compare these values with
                 empirical results by means of statistical methods. We
                 also compare the performance of a generated function
                 with that of REL (David Lewin 1980), perhaps the most
                 successful similarity function in the field. The
                 achieved results are encouraging: some of the generated
                 functions are able to produce stronger correlations
                 with empirical data than REL. As a satisfying
                 by-product, the results hint at the fact that there may
                 be a connection between the perceived closeness of
                 pitch-class sets and Shepard's universal cognitive
                 models. While the present application context is
                 musical set theory, we stress that similar procedures
                 can be applied to other areas of research as well.",
  notes =        "Also known as \cite{5357625}",

Genetic Programming entries for Atte Tenkanen