Subjective responses to wind farm noise: A review of laboratory listening test methods
Mahmoud A. Alamir,
Kristy L. Hansen,
Branko Zajamsek and
Peter Catcheside
Renewable and Sustainable Energy Reviews, 2019, vol. 114, issue C, -
Abstract:
Laboratory listening tests are a fundamental part of identifying and quantifying human perceptual responses to noise, including wind farm noise (WFN); a low-frequency environmental noise with time-varying components with the potential to impact more people as wind farm facilities continue to expand worldwide. Design characteristics of WFN listening tests vary between studies. This likely impacts WFN listening test results and makes quantitative comparisons difficult between studies. Accordingly, this paper reviews the available literature regarding WFN listening test methods, their overall characteristics and potentially important differences in noise stimuli and rating methods used. The key design variables explored include participant selection, stimuli duration, signal synthesis methods, noise reproduction methods and listening room characteristics. Listening test results from studies that have investigated the perceptual effects of various WFN components such as low-frequency noise and infrasound, tonality and amplitude modulation are presented. The impact on listening tests of factors unrelated to noise such as sensory acuity and sensitivity, attitudes/beliefs and visual effects are also explored. It is shown that some WFN characteristics have received limited attention to date in listening tests. These include broadband low-frequency noise, tonal noise, tonal amplitude modulation and amplitude modulation parameters such as modulation frequency and intermittency. The relative importance of acoustic and non-acoustic factors to human perception is also largely unknown and requires well-designed listening tests to help elucidate.
Keywords: Wind energy; Environmental noise; Wind farm noise; Amplitude modulation; Low-frequency noise; Listening tests; Annoyance (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.rser.2019.109317
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