On the Integration of Loudness Over Time for the Prediction of Single-Event Annoyance

<p dir="ltr">Loudness is the quality of human perception that is most related to acoustic intensity</p><p dir="ltr">and energy, however there is no agreed-upon way of integrating loudness over time for</p><p dir="ltr">the prediction of noise-induced annoyance. Most noise metrics used today for aircraft certification</p><p dir="ltr">and regulation employ the “Equal-Energy Hypothesis” (EEH) and sum the acoustical</p><p dir="ltr">energy of one or more noise events over time to create a single metric value that represents</p><p dir="ltr">the entire exposure. Using the EEH creates a natural tradeoff between the peak energy</p><p dir="ltr">and the duration of a single noise event, the “hypothesis” being that this tradeoff optimally</p><p dir="ltr">predicts annoyance in a wide variety of situations. This work proposes a strategy for integrating</p><p dir="ltr">a loudness-like time series which is flexible in two ways: First, it includes a parameter</p><p dir="ltr">b ∈ [0,1]. When b = 0, the metric will return the peak of the time series. At b = .5, the metric</p><p dir="ltr">will behave in accordance with the EEH. At b = 1, the metric will penalize the duration of the</p><p dir="ltr">sound more than the EEH would. Second, transformations are given so that the strategy can</p><p dir="ltr">be computed from, or generate quantities in units analogous to, decibels (or phon), physical</p><p dir="ltr">units (acoustic pressure/power), or perceptually-scaled units (sone). This approach is</p><p dir="ltr">demonstrated on a dataset of annoyance responses to single events of UAV and road vehicle</p><p dir="ltr">noise that is fit with an augmented linear regression. Analyses based on this integration</p><p dir="ltr">of A-weighted level and the output of the “Zwicker” loudness model yield similar results: that</p><p dir="ltr">subjects may have been slightly more sensitive to the durations of the events than the EEH</p><p dir="ltr">would suppose, but that the EEH cannot be disproven using these data. The time-integrated</p><p dir="ltr">metrics outperform both time-averaged and centile-based metrics.</p>