For the past several years, researchers at NASA Langley have been engaged in a series of projects to study the degree to which existing facilities and capabilities, originally created for work on full-scale aircraft, are extensible to smaller scales-those of the small unmanned aerial systems (sUAS, also UAVs and, colloquially, 'drones') that have been showing up in the nation's airspace of late. This paper follows an effort that has led to an initial human-subject psychoacoustic test regarding the annoyance generated by sUAS noise. This effort spans three phases: 1. The collection of the sounds through field recordings. 2. The formulation and execution of a psychoacoustic test using those recordings. 3. The initial analysis of the data from that test. The data suggests a lack of parity between the noise of the recorded sUAS and that of a set of road vehicles that were also recorded and included in the test, as measured by a set of contemporary noise metrics. Future work, including the possibility of further human subject testing, is discussed in light of this suggestion.
A psychoacoustic test was performed using simulated sounds from a distributed electric propulsion aircraft concept to help understand factors associated with human annoyance. A design space spanning the number of high-lift leading edge propellers and their relative operating speeds, inclusive of time varying effects associated with motor controller error and atmospheric turbulence, was considered. It was found that the mean annoyance response varies in a statistically significant manner with the number of propellers and with the inclusion of time varying effects, but does not differ significantly with the relative RPM between propellers. An annoyance model was developed, inclusive of confidence intervals, using the noise metrics of loudness, roughness, and tonality as predictors.
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.
The NASA Environmentally Responsible Aviation project has been successful in developing and demonstrating technologies for integrated aircraft systems that can simultaneously meet aggressive goals for fuel burn, noise and emissions. Some of the resulting systems substantially differ from the familiar tube and wing designs constituting the current civil transport fleet. This study attempts to explore whether or not the effective perceived noise level metric used in the NASA noise goal accurately reflects human subject response across the range of vehicles considered. Further, it seeks to determine, in a quantitative manner, if the sounds associated with the advanced aircraft are more or less preferable to the reference vehicles beyond any differences revealed by the metric. These explorations are made through psychoacoustic tests in a controlled laboratory environment using simulated stimuli developed from auralizations of selected vehicles based on systems noise assessments. Nomenclature d = time interval between t1 and t2 (s) PNLT = tone-corrected perceived noise level (PNdB) t1 = earliest time at which PNLT crosses PNLTmax-10, positive slope (s) t2 = latest time at which PNLT crosses PNLTmax-10, negative slope (s) tmax = time at PNLTmax (s) Δt = PNLT time increment (s)
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