  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.
