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

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Computational Phonogram Archiving

Part of the book series: Current Research in Systematic Musicology ((CRSM,volume 5))

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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.

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Notes

  1. 1.

    Music information retrieval encompasses computational methods for extracting, accessing and using information in collections music recordings. Examples include estimating the tempo of music in an audio recording, determining the key of a notated piece of music, and transcribing an audio music recording into notation.

  2. 2.

    A “dataset” is a collection of data. In our case, it is a collection of audio music recordings.

  3. 3.

    Calypso is a style of Caribbean music that originated in Trinidad and Tobago. For a more detailed description of Calypso, see for example [3].

  4. 4.

    http://www.transforming-musicology.org.

  5. 5.

    http://dml.city.ac.uk.

  6. 6.

    http://gtr.rcuk.ac.uk/project/3510829B-EAE9-48DC-A723-8093D92CAD60.

  7. 7.

    http://abcnotation.com/wiki/abc:standard:v2.1.

  8. 8.

    https://thesession.org/tunes/30.

  9. 9.

    http://archives.crem-cnrs.fr.

  10. 10.

    See for example [1, 4, 11].

  11. 11.

    In the dictionary of the English/creole of Trinidad and Tobago edited by Lise Winer soca is defined as “a type of calypso-based music, with a fast dance beat, and party lyrics.”.

  12. 12.

    http://archives.crem-cnrs.fr/.

  13. 13.

    http://telemeta.org/.

  14. 14.

    https://highnoongmt.wordpress.com/2018/01/05/volumes-1-20-of-folk-rnn-v1-transcriptions/.

  15. 15.

    Hultmark: https://www.youtube.com/watch?v=4kLxvJ-rXDs; Hughes: https://www.youtube.com/watch?v=GmwYtNgHW4g.

  16. 16.

    See further details in [17].

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Acknowledgements

Florabelle Spielmann, Ghislaine Glasson Deschaumes, Andrew Thompson.

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Authors and Affiliations

  1. Kingston University, London, UK

    Oded Ben-Tal

  2. Royal Institute of Technology KTH, Stockholm, Sweden

    Bob L. Sturm

  3. Universal Music Group, Santa Monica, USA

    Elio Quinton

  4. CREM-LESC, UMR7186, CNRS, Nanterre, France

    Josephine Simonnot & Aurelie Helmlinger

Authors
  1. Oded Ben-Tal
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  2. Bob L. Sturm
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  3. Elio Quinton
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  4. Josephine Simonnot
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  5. Aurelie Helmlinger
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Corresponding author

Correspondence to Bob L. Sturm .

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Editors and Affiliations

  1. Institute of Systematic Musicology, University of Hamburg, Hamburg, Germany

    Rolf Bader

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Ben-Tal, O., Sturm, B.L., Quinton, E., Simonnot, J., Helmlinger, A. (2019). Finding Music in Music Data: A Summary of the DaCaRyH Project. In: Bader, R. (eds) Computational Phonogram Archiving. Current Research in Systematic Musicology, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-02695-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-02695-0_9

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