Sound quality metric indicators of rotorcraft noise annoyance using multilevel 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 conducted in which subjects rated their annoyance levels to synthesized helicopter sounds. After controlling for loudness, a previous analysis using linear regression identified sharpness and tonality as important factors in predicting annoyance, followed by fluctuation strength. The 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, and the variation of regression parameters among subjects is evaluated using simple models. Then more complete models are investigated, which include the combination of selected metrics and subject-specific effects. While the conclusions from linear regression analysis are affirmed by multilevel analysis, other important effects emerge. In particular, subject-specific intercepts are shown to be more important than subject-specific slopes. In addition, subject-specific slopes for fluctuation strength and sharpness are more important than for tonality. Using a multilevel framework, the relative importance of sound quality metrics is reexamined, and the potential for modeling human annoyance to helicopter noise based on sound quality metrics is explored.