Variance fluctuations in nonstationary time series: a comparative study of music genres

TL;DR

This paper introduces a new method to analyze local variance scaling in nonstationary audio time series and applies it to different music genres, revealing genre-specific correlation properties.

Contribution

The study develops a novel technique for quantifying local variance in nonstationary signals and compares its application across various music genres.

Findings

Distinct correlation patterns identified for high art, popular, and dance music

Quantitative differences in variance fluctuations across genres

Method provides objective measures related to subjective musical experience

Abstract

An important problem in physics concerns the analysis of audio time series generated by transduced acoustic phenomena. Here, we develop a new method to quantify the scaling properties of the local variance of nonstationary time series. We apply this technique to analyze audio signals obtained from selected genres of music. We find quantitative differences in the correlation properties of high art music, popular music, and dance music. We discuss the relevance of these objective findings in relation to the subjective experience of music.