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Finding Music in Music Data: A Summary of the DaCaRyH Project

  • Oded Ben-Tal
  • Bob L. SturmEmail author
  • Elio Quinton
  • Josephine Simonnot
  • Aurelie Helmlinger
Chapter
Part of the Current Research in Systematic Musicology book series (CRSM, volume 5)

Abstract

The international research project, “Data science for the study of calypso-rhythm through history” (DaCaRyH), involved a collaboration between ethnomusicologists, computer scientists, and a composer. The primary aim of DaCaRyH was to explore how ethnomusicology could inform data science, and vice versa. Its secondary aim focused on creative applications of the results. This article summarises the results of the project, and more broadly discusses the benefits and challenges in such interdisciplinary research. It concludes with suggestions for reducing the barriers to similar work.

Notes

Acknowledgements

Florabelle Spielmann, Ghislaine Glasson Deschaumes, Andrew Thompson.

References

  1. 1.
    Aho WR (1987) Steel band music in Trinidad and Tobago: the creation of a people’s music. Lat Am Music Rev/Revista de Msica Latinoamericana 8(1):26–58CrossRefGoogle Scholar
  2. 2.
    Birth KK (2008) Bacchanalian sentiments; musical experiences and political counterpoints in Trinidad. Duke University PressGoogle Scholar
  3. 3.
    Cowley J (1998) Carnival, canboulay and calypso: traditions in the making. Cambridge University PressGoogle Scholar
  4. 4.
    Dudley S (2002) The steelband “Own Tune”: nationalism, festivity, and musical strategies in Trinidad’s panorama competition. Black Music Res J:13–36CrossRefGoogle Scholar
  5. 5.
    Dudley S (2008) Music from behind the bridge: steelband spirit and politics in Trinidad and Tobago (illustrated edition). Oxford University Press IncGoogle Scholar
  6. 6.
    Fillon T, Pellerin G, Brossier P, Simonnot J, La Dfense N (2014) An open web audio platform for ethnomusicological sound archives management and automatic analysis. In: Workshop on folk music analysis (FMA2014), p 36Google Scholar
  7. 7.
    Fink R (2013) Big (bad) data (editorial). Musicology now (online)Google Scholar
  8. 8.
    Goody J (1977) The domestication of the savage mind. Cambridge University PressGoogle Scholar
  9. 9.
    Guilbault J (2007) Governing sound: the cultural politics of Trinidad’s carnival musics. University of Chicago PressGoogle Scholar
  10. 10.
    Helmlinger A (2011) La virtuosité comme arme de guerre psychologique. Ateliers d’anthropologie 35Google Scholar
  11. 11.
    Helmlinger A (2012) Pan jumbie. Socit d’ethnologie, Mmoire sociale et musicale dans les steelbands (Trinidad et Tobago)Google Scholar
  12. 12.
    Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRefGoogle Scholar
  13. 13.
    Huron D (2013) On the virtuous and the vexatious in an age of big data. Music Percept 31(1):4–9CrossRefGoogle Scholar
  14. 14.
    van Koningsbruggen PH (1997) Trinidad carnival: a quest of national identity. CaribbeanGoogle Scholar
  15. 15.
    Marsden A (2015) Music similarity. In: Presentation at music similarity: concepts, cognition and computation. http://www.lorentzcenter.nl/lc/web/2015/669/presentations/Marsden.pptx
  16. 16.
    Mauch M, MacCallum RM, Levy M, Leroi AM (2015) The evolution of popular music: USA 1960–2010. R Soc Open Sci 2(5).  https://doi.org/10.1098/rsos.150081CrossRefGoogle Scholar
  17. 17.
    Quinton E, Spielmann F, Sturm BL (2017) Computational ethnomusicology for exploring trends in Trinidad steelband music through history. In: Proceedings of CMMRGoogle Scholar
  18. 18.
    Regis L (1999) The political calypso: true opposition in Trinidad and Tobago, 1962–1987. University Press of FloridaGoogle Scholar
  19. 19.
    Rouget G (1995) Ethnomusicologie d’un rituel. la représentation, ou de velasquez à francis bacon. L’Homme 35(133):77–97CrossRefGoogle Scholar
  20. 20.
    Schedl M, Gomez E, Urbano J (2014) Music information retrieval: recent developments and applications. Found Trends Inf Retr 8(2–3):127–261CrossRefGoogle Scholar
  21. 21.
    Schellenberg EG, von Scheve C (2012) Emotional cues in american popular music: five decades of the top 40. Psychol Aesthet Creat Arts 6(3):196–203CrossRefGoogle Scholar
  22. 22.
    Serra J, Corral A, Boguna M, Haro M, Arcos JL (2012) Measuring the evolution of contemporary western popular music. Sci Rep 2.  https://doi.org/10.1038/srep00521
  23. 23.
    Spielmann F, Helmlinger A, Simonnot J, Fillon T, Pellerin G, Sturm BL, Ben-Tal O, Quinton E (2017) Zoom arrière: L’ethnomusicologie à l’ère du Big Data. Cahiers d’ethnomusicologie 30:9–28Google Scholar
  24. 24.
    Stuempfle S (1995) The steelband movement: the forging of a national art in Trinidad and Tobago. University of Pennsylvania PressGoogle Scholar
  25. 25.
    Sturm BL (2014a) The state of the art ten years after a state of the art: future research in music information retrieval. J New Music Res 43(2):147–172CrossRefGoogle Scholar
  26. 26.
    Sturm BL (2014b) A survey of evaluation in music genre recognition. In: Nürnberger A, Stober S, Larsen B, Detyniecki M (eds) Adaptive multimedia retrieval: semantics, context, and adaptation, vol 8382, pp 29–66. LNCSGoogle Scholar
  27. 27.
    Sturm BL, Ben-Tal O (2017) Taking the models back to music practice: evaluating generative transcription models built using deep learning. J Creat Music Syst 2(1)Google Scholar
  28. 28.
    Sturm BL, Santos JF, Ben-Tal O, Korshunova I (2016) Music transcription modelling and composition using deep learning. In: Proceedings Conference Computer Simulation of Musical Creativity, Huddersfield, UKGoogle Scholar
  29. 29.
    Sturm BL, Ben-Tal O, Monaghan Ú, Collins N, Herremans D, Chew E, Hadjeres G, Deruty E, Pachet F (2018) Machine learning research that matters for music creation: a case study. J New Music Res (in press). https://doi.org/10.1080/09298215.2018.1515233CrossRefGoogle Scholar
  30. 30.
    Wallmark Z (2013) Big data and musicology: new methods, new questions. American musicological society national meeting, Pittsburgh, PA. Technical reportGoogle Scholar
  31. 31.
    Weiß C, Mauch M, Dixon S, Müller M (2018) Investigating style evolution of western classical music: a computational approach. Musicae ScientiaeGoogle Scholar
  32. 32.
    Weyde T, Cottrell S, Dykes J, Benetos E, Wolff D, Tidhar D, Kachkaev A, Plumbley M, Dixon S, Barthet M, Gold N, Abdallah S, Alancar-Brayner A, Mahey M, Tovell A (2014) Big data for musicology. In: Proceedings International Workshop on Digital Libraries for Musicology, New York, USAGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Oded Ben-Tal
    • 1
  • Bob L. Sturm
    • 2
    Email author
  • Elio Quinton
    • 3
  • Josephine Simonnot
    • 4
  • Aurelie Helmlinger
    • 4
  1. 1.Kingston UniversityLondonUK
  2. 2.Royal Institute of Technology KTHStockholmSweden
  3. 3.Universal Music GroupSanta MonicaUSA
  4. 4.CREM-LESC, UMR7186, CNRSNanterreFrance

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