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.
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References
Aucouturier J J, Pachet F (2002) Music similarity measures: What’s the use? In: Proceedings of the ISMIR International Conference on Music Information Retrieval
Cooper M, Foote J (2002) Automatic music summarization via similarity analysis. In: Proceedings of the ISMIR International Conference on Music Information Retrieval
Pampalk E (2006) Computational models of music similarity and their application in music information retrieval. PhD thesis, Vienna University of Technology
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
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
Rauber A, Pampalk E, Merkl D (2003) Journal of New Music Research 32: 193–210
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
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
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
Pampalk E (2003) A Matlab toolbox to compute music similarity from audio. In: Proceedings of the ISMIR International Conference on Music Information Retrieval
Bosteels K, Kerre E E (2007) Fuzzy Sets and Systems 158(22):2466–2479
Müller H, Müller W, McG Squire D, Marchand-Maillet S, Pun T (2001) Pattern Recognition Letters 22:593–601
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© 2008 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/978-3-540-76827-2_9
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