Computational approach to multifractal music

Abstract

In this work we perform a fractal analysis of 160 pieces of music belonging to six different genres. We show that the majority of the pieces reveal characteristics that allow us to classify them as physical processes called the 1/f (pink) noise. However, this is not true for classical music represented here by Frederic Chopin's works and for some jazz pieces that are much more correlated than the pink noise. We also perform a multifractal (MFDFA) analysis of these music pieces. We show that all the pieces reveal multifractal properties. The richest multifractal structures are observed for pop and rock music. Also the viariably of multifractal features is best visible for popular music genres. This can suggest that, from the multifractal perspective, classical and jazz music is much more uniform than pieces of the most popular genres of music.

15 pages, 6 figures

References in corpus (9)

• Multifractal detrended fluctuation analysis of nonstationary time series
• Multifractal Properties of Price Fluctuations of Stocks and Commodities
• Wavelet versus Detrended Fluctuation Analysis of multifractal structures
• The components of empirical multifractality in financial returns
• Multifractality in the stock market: price increments versus waiting times
• Components of multifractality in high-frequency stock returns
• Multifractal analysis of DNA walks and trails
• Boundary criticality and multifractality at the 2D spin quantum Hall transition
• Uncovering latent singularities from multifractal scaling laws in mixed asymptotic regime. Application to turbulence

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• New measure of multifractality and its application in finances