10.5061/DRYAD.6737V
González-Espinoza, Alfredo
Universidad Autónoma del Estado de Morelos
Larralde, Hernan
National Autonomous University of Mexico
Martinez-Mekler, Gustavo
National Autonomous University of Mexico
Mueller, Markus
Universidad Autónoma del Estado de Morelos
Data from: Multiple scaling behavior and nonlinear traits in music scores
Dryad
dataset
2017
Detrended Fluctuation Analysis
Nonlinear Correlations
Music Scores
time series
2017-11-13T14:18:40Z
2017-11-13T14:18:40Z
en
https://doi.org/10.1098/rsos.171282
6760509 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
We present a statistical analysis of music scores from different composers
using detrended fluctuation analysis. We find different fluctuation
profiles that correspond to distinct auto-correlation structures of the
musical pieces. Further, we reveal evidence for the presence of nonlinear
auto-correlations by estimating the detrended fluctuation analysis of the
magnitude series, a result validated by a corresponding study of
appropriate surrogate data. The amount and the character of nonlinear
correlations vary from one composer to another. Finally, we performed a
simple experiment in order to evaluate the pleasantness of the musical
surrogate pieces in comparison with the original music and find that
nonlinear correlations could play an important role in the aesthetic
perception of a musical piece.
SI-masterSupporting Information, here are the graphs for the fluctuation
functions. The graphs are labeled as following: Palestrina Motets:
PalestrinaMo-# Bach Musical Offering: BachMO-# Bach Well Tempered Clavier:
BachWTK-# Haydn String Quartets: HaydnSQ-# Mozart Piano Sonatas:
MozartPS-# Mozart String Quartets: MozartSQ-# Beethoven Fugues:
BeethovenF-# Beethoven Early Quartets: BeethovenEQ-# Beethoven Late
Quartets: BeethovenLQ-# Dvorak Humoresques: DvorakH-# Dvorak Silhouettes:
DvorakS-# Dvorak Serenade for Strings: DvorakSt-# Shostakovich Preludes
and Fugues: ShostakovichPF-# The number corresponds to the place in the
full list of opus. Files corresponding to a scaling behavior over all s
boxes (profile 1) are in Scaling.pdf, files corresponding to the functions
showing a crossover (profiles 1 and 2) are in Crossovers.pdf, the files
with the graps of profiles 4 and 5 are in NoScaling.pdf The values of the
alpha's and R squared for the linear fitting in the fluctuations
function are on the Tables: TableScaling.pdf corresponds to the functions
following a power law and TableCrossovers.pdf to the functions with a
crossover.InfoSeries.jl-masterRepository for every calculation of the DFA
and the construction of the time series from the .csv file. The repository
can also be consulted in GitHub:
https://github.com/spiralizing/InfoSeries.jlSupplementary MaterialFull
list of opus analyzed, details for the construction of the time series,
individual DFA functions and their relation with the power spectra.
Description of the survey.SupplementaryMaterial.pdfSM-TimeSeriesAll time
series analyzed and the four tracks of the Survey. The time series were
constructed as described in the Supplementary Material, from MIDIs
downloaded from the web databases mentioned in references.