The Effective Perceived Noise Level (EPNL) is the primary metric used for assessing subjective response to aircraft noise. The EPNL comprises calculation of the Perceived Noise Level (in PNdB), and takes into account flyover duration and the presence of pure tones to arrive at an adjusted EPNL value. With the presence of a single significant tone, EPNL has been found to be reasonably effective for the assessment of aircraft noise annoyance. Several authors have, however, suggested that EPNL is not capable of quantifying the subjective response to aircraft noise that contains multiple complex tones. The noise source referred to as "Buzz-saw" noise is a typical example of complex tonal content in aircraft noise with an important effect on both cabin and community noise impact. This paper presents the results of a series of listening tests where a number of participants were exposed to samples of aircraft noise with six variants of aircraft engines, assumed representative of the contemporary twin engine aircraft fleet. On the basis of the findings of these listening tests, the Aures tonality method significantly outperforms the EPNL tone correction method when assessing the subjective response to aircraft noise during takeoff with the presence of multiple complex tones. The participants reported 'high pitch' as one of the least preferable aircraft noise characteristics, and consequently, the psychoacoustics metric Sharpness was found to be another important contributor to subjective response to the noise of two specific aircraft engine groups (out of the six considered). The limitations of Aures tonality are discussed, in particular for aircraft noise with both a series of complex tones spaced evenly across the frequency spectrum with relatively even sound levels and less subjectively dominant single frequency tones (compared to broadband noise). In line with these limitations, further work is proposed for more effective assessment of subjective response to aircraft noise containing significant tonal content in the form of numerous closely spaced or other complex tones.
The number of applications for drones under R&D have growth significantly during the last few years; however, the wider adoption of these technologies requires ensuring public trust and acceptance. Noise has been identified as one of the key concerns for public acceptance. Although substantial research has been carried out to better understand the sound source generation mechanisms in drones, important questions remain about the requirements for operational procedures and regulatory frameworks. An important issue is that drones operate within different airspace, closer to communities than conventional aircraft, and that the noise produced is highly tonal and contains a greater proportion of high-frequency broadband noise compared with typical aircraft noise. This is likely to cause concern for exposed communities due to impacts on public health and well-being. This paper presents a modelling framework for setting recommendations for drone operations to minimise community noise impact. The modelling framework is based on specific noise targets, e.g., the guidelines at a receiver position defined by WHO for sleep quality inside a residential property. The main assumption is that the estimation of drone noise exposure indoors is highly relevant for informing operational constraints to minimise noise annoyance and sleep disturbance. This paper illustrates the applicability of the modelling framework with a case study, where maximum A-weighted sound pressure levels LAmax and sound exposure levels SEL as received in typical indoor environments are used to define drone-façade minimum distance to meet WHO recommendations. The practical and scalable capabilities of this modelling framework make it a useful tool for inferring and assessing the impact of drone noise through compliance with appropriate guideline noise criteria. It is considered that with further refinement, this modelling framework could prove to be a significant tool in assisting with the development of noise metrics, regulations specific to drone operations and the assessment of future drone operations and associated noise.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.