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A Psychoacoustic-Based Methodology for Sound Mass Music Analysis

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Music in the AI Era (CMMR 2021)

Abstract

A sound mass is a specific state of the musical texture corresponding to a large number of sound events concentrated within a short time and/or frequency interval. Conceptually, it is associated with the work of György Ligeti, Krzysztof Penderecki, and Iannis Xenakis, among others. Recent studies have investigated sound masses via perceptual models, such as Gestalt models of perception and auditory scene analysis, and also from a more acoustic and psychoacoustic perspective obtained through audio recordings. The main goal of this paper is to propose a methodology for the musical analysis of sound mass music through audio recordings combined with other research sources from music theory, musical analysis and psychoacoustics. We apply this method in the analysis of a recording of the first movement of Ligeti’s Ten Pieces for Wind Quintet (1968), and explore relationships between the obtained audio descriptors and Ligeti’s concepts of timbre of movement and permeability, in order to reveal Ligeti’s strategies when dealing with musical texture and sound masses.

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Notes

  1. 1.

    In the original, Ligeti uses timbre du mouvement in French and Bewegungsfarbe in German [22, p. 169].

  2. 2.

    From this point onwards this abbreviation will be used instead of Ten Pieces for Wind Quintet.

  3. 3.

    Bark is the unit of Zwicker’s critical bandwidth model [43].

  4. 4.

    For a full revision on roughness curves, see [37].

  5. 5.

    https://www.python.org/.

  6. 6.

    https://jupyter.org/.

  7. 7.

    https://gitlab.com/Feulo/ligetis-wind-quintet-analysis.

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Acknowledgments

Micael Antunes is supported by FAPESP Grant 2019/09734-3 and 2021/11880-8, Jônatas Manzolli is supported by CNPq Grant 304431/2018-4 and 429620/2018-7 and Marcelo Queiroz is supported by CNPq Grant 307389/2019-7.

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Antunes, M., do Espirito Santo, G.F., Manzolli, J., Queiroz, M. (2023). A Psychoacoustic-Based Methodology for Sound Mass Music Analysis. In: Aramaki, M., Hirata, K., Kitahara, T., Kronland-Martinet, R., Ystad, S. (eds) Music in the AI Era. CMMR 2021. Lecture Notes in Computer Science, vol 13770 . Springer, Cham. https://doi.org/10.1007/978-3-031-35382-6_21

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