Aircraft noise-induced annoyance analysis using psychoacoustic listening experiments

Abstract

Aircraft noise annoyance is inherently subjective, and its accurate quantification represents a challenging task. There is a lack of consensus in the scientific community regarding which metrics are best for effectively representing this type of annoyance. The present study aims at relating various sound metrics to noise annoyance ratings measured in listening experiments featuring 60 aircraft flyover recordings (30 landings and 30 takeoffs). This is done by considering different sound quality metrics (SQMs), psychoacoustic annoyance models, and more conventional noise certification metrics, such as the effective perceived noise level (EPNL) or the sound exposure level. A correlation analysis was subsequently performed on a large pool of sound metrics considering both linear and non-linear functions. The results show that, in general, metrics derived from psychoacoustic annoyance models (especially those proposed by Zwicker and Di et al.) present considerably better correlations compared to conventional metrics and most individual SQMs. The metrics of loudness, EPNL, and maximum perceived noise level (PNL) also exhibit strong correlations and capacity to predict a substantial portion of the variance observed in the reported annoyance ratings. Moreover, considering non-linear functions (e.g. logarithmic or hyperbolic tangent power) further improves the prediction performance.