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Structural Similarity Based on Time-Span Sub-Trees

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9110))

Abstract

We propose structural similarity of two melodies based on sub-trees from the time-span tree provided by the Generative Theory of Tonal Music. The structural distance of the tree was previously defined and called the “maximal time-span distance,” and experimental results showed to some extent a correspondence between the maximal time-span distance and psychological similarity. However, there is a big problem in that almost all pairs of melodies are not similar on the basis of the maximal time-span distance because the definition of the similarity is too strict. Therefore, we attempt to express a melodic structural similarity by using the coincidence rate of time-span sub-trees to weaken the condition for calculating similarity. We have set up three experimental conditions and compared their results.

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Notes

  1. 1.

    This definition of similarity is different from general definitions such as of the Jaccard coefficient, the Simpson coefficient, and Dice coefficient in that we independently normalize \(P-P \sqcap Q\) and \(Q-P \sqcap Q\); otherwise, only one melody is influential when one melody has a lot of notes and the other has little numbers of notes.

  2. 2.

    The distance by maximal time-span has been improved in [6], however, we here omit its detail by our limited space. We will compare this improved distance by maximal time-span with our sub-tree similarity in future.

References

  1. Lerdahl, F., Jackendoff, R.: A Generative Theory of Tonal Music. MIT Press, Cambridge (1983)

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  2. Hamanaka, M., Hirata, K., Tojo, S.: Implementing “A generative theory of tonal music”. J. New Music Res. 35(4), 249–277 (2007)

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  3. Hamanaka, M., Hirata, K., Tojo, S.: Melody morphing method based on GTTM. In: Proceeding of ICMC 2008, Belfast, pp. 155–158 (2008)

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  4. Tojo, S., Hirata, K.: Structural similarity based on time-span tree. In: Aramaki, M., Barthet, M., Kronland-Martinet, R., Ystad, S. (eds.) CMMR 2012. LNCS, vol. 7900, pp. 400–421. Springer, Heidelberg (2013)

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  5. Hamanaka, M., Hirata, K., Tojo, S.: Music structural analysis database based on GTTM. In: Proceeding of ISMIR 2014, Taipei, pp. 325–330 (2014)

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  6. Hirata, K., Tojo, S., Hamanaka, M.: Cognitive similarity grounded by tree distance from the analysis of K.265/300e. In: Aramaki, M., Derrien, O., Kronland-Martinet, R., Ystad, S. (eds.) CMMR 2013. LNCS, vol. 8905, pp. 589–605. Springer, Heidelberg (2014)

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Acknowledgments

This work was supported in part by JSPS KAKENHI Grant Numbers 23500145, 25330434, and 25700036 and PRESTO, JST.

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Correspondence to Masatoshi Hamanaka .

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Hamanaka, M., Hirata, K., Tojo, S. (2015). Structural Similarity Based on Time-Span Sub-Trees. In: Collins, T., Meredith, D., Volk, A. (eds) Mathematics and Computation in Music. MCM 2015. Lecture Notes in Computer Science(), vol 9110. Springer, Cham. https://doi.org/10.1007/978-3-319-20603-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-20603-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20602-8

  • Online ISBN: 978-3-319-20603-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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