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An Experiment with Electric Guitar Signals for Exploring the Virtuosity based on the Entropy of Music

Igor Lugo, Martha G. Alatriste-Contreras

TL;DR

This study treats musical virtuosity as a collective property emerging in electric guitar performances by composer–performer guitarists and tests whether entropy of the audio spectrum can quantify virtuosity. Using FFT-based spectral decomposition, the authors fit a range of statistical distributions to spectrum magnitudes with Kolmogorov–Smirnov goodness-of-fit and quantify diversity with Shannon entropy. They compare two groups (legendary vs non-legend guitarists) and find that entropy values occupy a shared range, with no significant group-level difference, suggesting virtuosity corresponds to a range of spectral diversity rather than a single value. The work demonstrates that entropy-derived metrics can capture aspects of virtuosity in music and provides open data and reproducible analysis pipelines, while acknowledging the need for broader instrument- and genre-specific validation.

Abstract

We analyze the concept of virtuosity as a collective attribute in music and its relationship with the entropy based on an experiment that compares two sets of digital signals played by composer-performer electric guitarists. Based on an interdisciplinary approach related to the complex systems, we computed the spectrum of signals, identified statistical distributions that best describe them, and measured the Shannon entropy to establish their diversity. Findings suggested that virtuosity might be related to a range of entropy values that identify levels of diversity of the frequency components of audio signals. Despite the presence of different values of entropy in the two sets of signals, they are statistically similar. Therefore, entropy values can be interpreted as levels of virtuosity in music.

An Experiment with Electric Guitar Signals for Exploring the Virtuosity based on the Entropy of Music

TL;DR

This study treats musical virtuosity as a collective property emerging in electric guitar performances by composer–performer guitarists and tests whether entropy of the audio spectrum can quantify virtuosity. Using FFT-based spectral decomposition, the authors fit a range of statistical distributions to spectrum magnitudes with Kolmogorov–Smirnov goodness-of-fit and quantify diversity with Shannon entropy. They compare two groups (legendary vs non-legend guitarists) and find that entropy values occupy a shared range, with no significant group-level difference, suggesting virtuosity corresponds to a range of spectral diversity rather than a single value. The work demonstrates that entropy-derived metrics can capture aspects of virtuosity in music and provides open data and reproducible analysis pipelines, while acknowledging the need for broader instrument- and genre-specific validation.

Abstract

We analyze the concept of virtuosity as a collective attribute in music and its relationship with the entropy based on an experiment that compares two sets of digital signals played by composer-performer electric guitarists. Based on an interdisciplinary approach related to the complex systems, we computed the spectrum of signals, identified statistical distributions that best describe them, and measured the Shannon entropy to establish their diversity. Findings suggested that virtuosity might be related to a range of entropy values that identify levels of diversity of the frequency components of audio signals. Despite the presence of different values of entropy in the two sets of signals, they are statistically similar. Therefore, entropy values can be interpreted as levels of virtuosity in music.
Paper Structure (63 sections, 2 equations, 9 figures, 16 tables)

This paper contains 63 sections, 2 equations, 9 figures, 16 tables.

Table of Contents

  1. See the supplementary information document.
  2. S1 Audio.
  3. S2 Audio.
  4. S1 Video.
  5. S2 Video.
  6. S3 Video.
  7. S4 Video.
  8. S5 Video.
  9. S6 Video.
  10. S7 Video.
  11. S8 Video.
  12. S9 Video.
  13. S10 Video.
  14. S11 Video.
  15. S12 Video.
  16. ...and 48 more sections

Figures (9)

  • Figure 1: Flowchart of the experimental design.
  • Figure 2: Arpeggio.(a) Waveform, (b) Spectrum in a linear scale, and (c) log-log scale. Resource: https://freesound.org/people/Lucks86/sounds/246268/
  • Figure 3: Gaussian noise.(a) Waveform, (b) Spectrum in a linear scale, and (c) log-log scale. Resource: https://freesound.org/people/Jace/sounds/35291/.
  • Figure 4: Number of guitar players in the trial and the control group associated with the best fit statistical distribution. See the Supplementary Information, Table \ref{['tableBFTrial']} and \ref{['tableBFControl']}, for details of the estimated parameters and the KS goodness of fit test values. See the Supplementary Information, Table \ref{['pdfs']} for the probability density functions (PDF) associated with the statistical distributions.
  • Figure 5: Diversity according to the trial and control groups of guitar players and their best fit statistical distribution. Subfig (a) and (b) show the guitarists and their best fit of the trial and control groups from low to high values. The arpeggio and the Gaussian noise are the lowest and highest values in diversity respectively. Subfig (c) shows the comparison of the trial and control groups by pair of guitarists without the arpeggio and the Gaussian noise value.
  • ...and 4 more figures