Free access to paper on nonparametric estimation of music signals


  • Author: Statistics Views
  • Date: 14 May 2018

Each week, we select a recently published article and provide free access. This week's is from Australian & New Zealand Journal of Statistics and is available from the latest issue.

Nonparametric estimation of the dynamic range of music signals

Pietro Coretto and Francesco Giordano

Australian & New Zealand Journal of Statistics, Vol 59, Issue 4, pp.389-412


The introduction is provided below:

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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 of catching transient energy. This component is recovered by a simple rate‐optimal kernel estimator that requires a single data‐driven tuning parameter. 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 behaviour of the statistic is assessed based on a large numerical experiment where we simulate dynamic compression on a selection of real‐world 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.

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