Skip to main content

Fuzzy Audio Similarity Measures Based on Spectrum Histograms and Fluctuation Patterns

  • Chapter
Computational Intelligence in Multimedia Processing: Recent Advances

Part of the book series: Studies in Computational Intelligence ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aucouturier J J, Pachet F (2002) Music similarity measures: What’s the use? In: Proceedings of the ISMIR International Conference on Music Information Retrieval

    Google Scholar 

  2. Cooper M, Foote J (2002) Automatic music summarization via similarity analysis. In: Proceedings of the ISMIR International Conference on Music Information Retrieval

    Google Scholar 

  3. Pampalk E (2006) Computational models of music similarity and their application in music information retrieval. PhD thesis, Vienna University of Technology

    Google Scholar 

  4. Pampalk E, Dixon S, Widmer G (2003) Exploring music collections by browsing different views. In: Proceedings of the ISMIR International Conference on Music Information Retrieval

    Google Scholar 

  5. Pampalk E, Rauber A, Merkl D (2002) Content-based organization and visualization of music archives. In: Proceedings of the ACM International Conference on Multimedia, 570–579

    Google Scholar 

  6. Rauber A, Pampalk E, Merkl D (2003) Journal of New Music Research 32: 193–210

    Article  Google Scholar 

  7. Logan B, Salomon A (2001) A music similarity function based on signal analysis. In: Proceedings of the International Conference on Multimedia and Expo, 745–748

    Google Scholar 

  8. Mandel M, Ellis D (2005) Song-level features and support vector machines for music classification. In: Proceedings of the ISMIR International Conference on Music Information Retrieval

    Google Scholar 

  9. Pampalk E, Dixon S, Widmer G (2003) On the evaluation of perceptual similarity measures for music. In: Proceedings of the International Conference on Digital Audio Effects, 7–12

    Google Scholar 

  10. Pampalk E (2003) A Matlab toolbox to compute music similarity from audio. In: Proceedings of the ISMIR International Conference on Music Information Retrieval

    Google Scholar 

  11. Bosteels K, Kerre E E (2007) Fuzzy Sets and Systems 158(22):2466–2479

    Article  MATH  MathSciNet  Google Scholar 

  12. Müller H, Müller W, McG Squire D, Marchand-Maillet S, Pun T (2001) Pattern Recognition Letters 22:593–601

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bosteels, K., Kerre, E.E. (2008). Fuzzy Audio Similarity Measures Based on Spectrum Histograms and Fluctuation Patterns. In: Hassanien, AE., Abraham, A., Kacprzyk, J. (eds) Computational Intelligence in Multimedia Processing: Recent Advances. Studies in Computational Intelligence, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76827-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76827-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76826-5

  • Online ISBN: 978-3-540-76827-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics