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Information content of note transitions in the music of J. S. Bach

Suman Kulkarni, Sophia U. David, Christopher W. Lynn, and Dani S. Bassett
Phys. Rev. Research 6, 013136 – Published 2 February 2024
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Abstract

Music has a complex structure that expresses emotion and conveys information. Humans process that information through imperfect cognitive instruments that produce a gestalt, smeared version of reality. How can we quantify the information contained in a piece of music? Further, what is the information inferred by a human, and how does that relate to (and differ from) the true structure of a piece? To tackle these questions quantitatively, we present a framework to study the information conveyed in a musical piece by constructing and analyzing networks formed by notes (nodes) and their transitions (edges). Using this framework, we analyze music composed by J. S. Bach through the lens of network science, information theory, and statistical physics. Regarded as one of the greatest composers in the Western music tradition, Bach's work is highly mathematically structured and spans a wide range of compositional forms, such as fugues and choral pieces. Conceptualizing each composition as a network of note transitions, we quantify the information contained in each piece and find that different kinds of compositions can be grouped together according to their information content and network structure. Moreover, using a model for how humans infer networks of information, we find that the music networks communicate large amounts of information while maintaining small deviations of the inferred network from the true network, suggesting that they are structured for efficient communication of information. We probe the network structures that enable this rapid and efficient communication of information—namely, high heterogeneity and strong clustering. Taken together, our findings shed light on the information and network properties of Bach's compositions. More generally, our simple framework serves as a stepping stone for exploring further musical complexities, creativity, and questions therein.

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  • Received 6 September 2023
  • Accepted 6 December 2023

DOI:https://doi.org/10.1103/PhysRevResearch.6.013136

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

NetworksInterdisciplinary PhysicsStatistical Physics & ThermodynamicsPhysics of Living Systems

Arts & Culture

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Measuring the Information Delivered by Music

Published 2 February 2024

A network-theory model, tested on the work of Johann Sebastian Bach, offers tools for quantifying the amount of information delivered to a listener by a musical piece.

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Authors & Affiliations

Suman Kulkarni1,*, Sophia U. David2,3, Christopher W. Lynn4,5, and Dani S. Bassett1,2,6,7,8,9,†

  • 1Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 2Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 3Department of Psychology, Yale University, New Haven, Connecticut 06520, USA
  • 4Initiative for the Theoretical Sciences, Graduate Center, City University of New York, New York, New York 10016, USA
  • 5Joseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA
  • 6Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 7Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 8Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  • 9Santa Fe Institute, Santa Fe, New Mexico 87501, USA

  • *Corresponding author: sumank@sas.upenn.edu
  • Corresponding author: dsb@seas.upenn.edu

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Vol. 6, Iss. 1 — February - April 2024

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