The dynamic range is an important parameter which measures the spread of
sound power, and for music signals it is a measure of recording quality. There
are various descriptive measures of sound power, none of which has strong
statistical foundations. We start from a nonparametric model for sound waves
where an additive stochastic term has the role to catch transient energy. This
component is recovered by a simple rate-optimal kernel estimator that requires
a single data-driven tuning. The distribution of its variance is approximated
by a consistent random subsampling method that is able to cope with the massive
size of the typical dataset. Based on the latter, we propose a statistic, and
an estimation method that is able to represent the dynamic range concept
consistently. The behavior of the statistic is assessed based on a large
numerical experiment where we simulate dynamic compression on a selection of
real music signals. Application of the method to real data also shows how the
proposed method can predict subjective experts' opinions about the hifi quality
of a recording
