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Fuzzy Audio Similarity Measures Based on Spectrum Histograms and Fluctuation Patterns

  • Klaas Bosteels
  • Etienne E. Kerre
Part of the Studies in Computational Intelligence book series (SCI, volume 96)

Spectrum histograms and fluctuation patterns are representations of audio fragments. By comparing these representations, we can determine the similarity between the corresponding fragments. Traditionally, this is done using the Euclidean distance. In this chapter, however, we study an alternative approach, namely, comparing the representations by means of fuzzy similarity measures. Once the preliminary notions have been addressed, we present a recently introduced triparametric family of fuzzy similarity measures, together with several constraints on its parameters that warrant certain potentially desirable or useful properties. In particular, we present constraints for several forms of restrictability, which allow to reduce the computation time in practical applications. Next, we use some members of this family to construct various audio similarity measures based on spectrum histograms and fluctuation patterns. To conclude, we analyse the performance of the constructed audio similarity measures experimentally.

Keywords

Feature Vector Similarity Measure Test Collection Fluctuation Pattern Music Information Retrieval 
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 2008

Authors and Affiliations

  • Klaas Bosteels
    • 1
  • Etienne E. Kerre
    • 1
  1. 1.Fuzziness and Uncertainty Modelling Research Group Department of Applied Mathematics and Computer ScienceGhent UniversityGentBelgium

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