Informetric analysis of a music database

Michael Nelson () and J. Stephen Downie
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Michael Nelson: Univ. of Western Ontario
J. Stephen Downie: University of Illinois at Urbana-Champaign

Scientometrics, 2002, vol. 54, issue 2, No 6, 243-255

Abstract: Abstract We analyse the statistical properties a database of musical notes for the purpose of designing an information retrieval system as part of the Musifind project. In order to reduce the amount of musical information we convert the database to the intervals between notes, which will make the database easier to search. We also investigate a further simplification by creating equivalence classes of musical intervals which also increases the resilience of searches to errors in the query. The Zipf, Zipf-Mandelbrot, Generalized Waring (GW) and Generalized Inverse Gaussian-Poisson (GIGP) distributions are tested against these various representations with the GIGP distribution providing the best overall fit for the data. There are many similarities with text databases, especially those with short bibliographic records. There are also some differences, particularly in the highest frequency intervals which occur with a much lower frequency than the highest frequency “stopwords” in a text database. This provides evidence to support the hypothesis that traditional text retrieval methods will work for a music database.

Date: 2002
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DOI: 10.1023/A:1016013912188

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