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Beats01:09

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The study of music provides many examples of the superposition of waves and the constructive and destructive interference that occurs. Very few examples of music being performed consist of a single source playing a single frequency for an extended period of time. A single frequency of sound for an extended period might be monotonous to the point of irritation, similar to the unwanted drone of an aircraft engine or a loud fan. Music is pleasant and exciting due to mixing the changing frequencies...
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The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent...
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If a driven oscillator needs to resonate at a specific frequency, then very light damping is required. An example of light damping includes playing piano strings and many other musical instruments. Conversely, to achieve small-amplitude oscillations as in a car's suspension system, heavy damping is required. Heavy damping reduces the amplitude, but the tradeoff is that the system responds at more frequencies. Speed bumps and gravel roads prove that even a car's suspension system is not...
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A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
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Synchronization in human musical rhythms and mutually interacting complex systems.

Holger Hennig1

  • 1Department of Physics, Harvard University, Cambridge, MA 02138 holgerh@nld.ds.mpg.de.

Proceedings of the National Academy of Sciences of the United States of America
|August 13, 2014
PubMed
Summary
This summary is machine-generated.

Human rhythmic synchronization shows scale-free cross-correlations, meaning past beats influence future ones for minutes. This finding applies to music and other complex systems.

Keywords:
anticorrelationsinterbrain synchronizationlong-range cross-correlationsmusical couplingtime series analysis

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Area of Science:

  • Neuroscience
  • Complex Systems Science
  • Music Cognition

Background:

  • Rhythmic synchronization is fundamental to musical interaction.
  • Ensemble music is influenced by genre, skill, and interpretation.
  • Understanding the statistical nature of human rhythmic synchronization is key.

Purpose of the Study:

  • To investigate the statistical properties of mutual interaction in human rhythmic synchronization.
  • To explore the temporal dependencies in interbeat intervals between individuals.
  • To develop a model explaining observed synchronization phenomena.

Main Methods:

  • Analysis of interbeat intervals from human participants (laypeople and musicians).
  • Statistical analysis to identify cross-correlations and scale-free properties.
  • Development of a general stochastic model for mutually interacting complex systems.

Main Results:

  • Interbeat intervals exhibit scale-free (power law) cross-correlations in both laypeople and musicians.
  • A long-term memory effect was observed, with dependencies extending over several minutes.
  • A proposed stochastic model provides a physiologically motivated explanation for scale-free cross-correlations.

Conclusions:

  • Rhythmic synchronization displays scale-free cross-correlations and long-term memory.
  • The findings have implications for understanding interbrain synchronization and can be applied to electronic music production.
  • The developed model is applicable to diverse complex systems exhibiting scale-free cross-correlations.