Sound quality metric indicators of rotorcraft noise annoyance using multilevel regression analysis

Abstract

It is hypothesized that sound quality metrics, particularly loudness, sharpness, tonality, impulsiveness, fluctuation strength, and roughness, could all be possible indicators of the reported annoyance to helicopter noise. To test this hypothesis, a psychoacoustic test was recently conducted in which subjects rated their annoyance levels to synthesized helicopter sounds [Krishnamurthy, InterNoise2018, Paper 1338]. After controlling for loudness, linear regression identified sharpness and tonality as important factors in predicting annoyance, followed by fluctuation strength. Current work focuses on multilevel regression techniques in which the regression slopes and intercepts are assumed to take on normal distributions across subjects. The importance of each metric is evaluated one-by-one, and the variation among subjects is evaluated using simple models. Then, more complete models are investigated, which include the combination of selected metrics and random effects. While the conclusions from linear regression analysis are affirmed by multilevel analysis, other important effects emerge. In particular, a random intercept is shown to be more important than a random slope. In this framework, the relative importance of sound quality metrics is re-examined, and the potential for the modeling of human annoyance to helicopter noise based on sound quality metrics is explored.

A Psychoacoustic Approach to Building Knowledge about Human Response to Noise of Unmanned Aerial Vehicles

“…Tonality is a metric aimed at identifying and quantifying the perceptual effects of tonal components in a given sound. Sharpness, tonality and fluctuation strength have been found well correlated with rotorcraft noise annoyance [ 30 , 31 ]. In a study investigating the noise annoyance of hovering UAVs, Gwak et al [ 14 ] found that the noise annoyance of UAVs (with MTOM ranging from 113.5 g to 11 kg) was highly related to loudness, sharpness and fluctuation strength.…”

Effects of a hovering unmanned aerial vehicle on urban soundscapes perception

“…LAeq, drone noise source and visual scene, on the reported loudness, annoyance and pleasantness was evaluated using a "one-off" approach. In this approach, the importance of each factor is assessed based on model accuracy when removing it from the analysis (Boucher et al, 2019). Three multilevel linear regression models were tested, M1 (fixed intercept, fixed slopes), M2 (fixed intercept, variable slopes) and M3…”

An investigation into the impact of unmanned aerial vehicles on soundscape perception in urban and rural environments

“…Stage 3 consists of a multilevel linear regression analysis, to identify the significance of subjectdependent responses of perceived annoyance. Multilevel linear regression has been used previously to investigate the factors contributing to annoyance for rotorcraft and small UAV, and has found to be a useful tool in discovering key variables [4] [7]. Multilevel linear regression is a method that integrates no pooling and complete pooling of data between subjects.…”

“…𝛾 𝑛0 , which does not vary per subject, is the regression coefficient of the 𝑛-th SQM 5 th percentile value of sound 𝑖. Furthermore, introducing subject-dependent regression slope coefficients for each SQM can reveal more information about the variance between how participants perceive these metrics, but previous literature has found that introducing subject-dependent slopes for this style of SQM analysis yielded little improvement to model accuracy when compared to the increase in accuracy introduced by including subject-dependent intercepts [7]. Therefore, a subject-dependent slope and intercept model was omitted from this analysis.…”

“…Impulsiveness quantifies the presence of sudden changes in the sound level perceived by the ear. It has been shown that metrics such as sharpness, tonality and fluctuation strength are strongly correlated with the perceived annoyance of rotorcraft, and hence may be significant indicators of the perceived annoyance to drone noise [7]. Research has also proven that the loudness, sharpness, and fluctuation strength were derived to be statistically significant variables in predicting the perceived annoyance of hovering drone noise [4].…”