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Modeling Human Experts’ Identification of Orchestral Blends Using Symbolic Information

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Perception, Representations, Image, Sound, Music (CMMR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12631))

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Abstract

Orchestral blend happens when sounds coming from two or more instruments are perceived as a single sonic stream. Several studies have suggested that different musical properties contribute to create such an effect. We developed models to identify orchestral blend effects from symbolic information taken from scores based on calculations related to three musical parameters, namely onset synchrony, pitch harmonicity, and parallelism in pitch and dynamics. In order to evaluate the performance of the models, we applied them to different orchestral pieces and compared the outputs with human experts’ ratings available in the Orchestration Analysis and Research Database (Orchard). Using different thresholds for the three parameters under consideration, the models were able to successfully retrieve 81% of the instruments involved in an orchestral blend on average. These results suggest that symbolic representation of music conveys perceptual information. However, further developments including audio analyses to take into account timbral properties could alleviate some of the current limitations.

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Notes

  1. 1.

    www.w3.org/2017/12/musicxml31/.

  2. 2.

    www.orchplaymusic.com.

  3. 3.

    https://docs.python.org/3/library/xml.etree.elementtree.html.

  4. 4.

    https://orchard.actor-project.org.

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Acknowledgments

This research is supported by a NSERC SPG-P/507004-17 research grant.

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Authors and Affiliations

  1. Schulich School of Music, McGill University, Montreal, QC, Canada

    Aurélien Antoine, Philippe Depalle, Philippe Macnab-Séguin & Stephen McAdams

Authors
  1. Aurélien Antoine
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  2. Philippe Depalle
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  3. Philippe Macnab-Séguin
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  4. Stephen McAdams
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Corresponding author

Correspondence to Aurélien Antoine .

Editor information

Editors and Affiliations

  1. Laboratoire PRISM, CNRS-AMU, Marseille, France

    Richard Kronland-Martinet

  2. Laboratoire PRISM, CNRS-AMU, Marseille, France

    Sølvi Ystad

  3. Laboratoire PRISM, CNRS-AMU, Marseille, France

    Mitsuko Aramaki

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Antoine, A., Depalle, P., Macnab-Séguin, P., McAdams, S. (2021). Modeling Human Experts’ Identification of Orchestral Blends Using Symbolic Information. In: Kronland-Martinet, R., Ystad, S., Aramaki, M. (eds) Perception, Representations, Image, Sound, Music. CMMR 2019. Lecture Notes in Computer Science(), vol 12631. Springer, Cham. https://doi.org/10.1007/978-3-030-70210-6_24

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  • DOI: https://doi.org/10.1007/978-3-030-70210-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70209-0

  • Online ISBN: 978-3-030-70210-6

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