A scoping-review-based experimental framework for assessing human responses to UAV noise

Bibliographic Data

ID21477063
AuthorsTianjing Feng (University College London), J Kang (0000-0001-8995-5636, University College London, corresponding author)
Year2026
Volume120
Pages108470
Publication date2026-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Impact Assessment Review (JOURNAL)
Journal identifiersISSN: 0195-9255 • E-ISSN: 1873-6432
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.eiar.2026.108470
OpenAlexW7160144637
LanguageEN
References cited46

Noise from UAVs (unmanned aerial vehicles) is increasingly relevant to UAV delivery and urban air mobility, yet perception evidence is often hard to interpret because experiments operationalise exposure as generic level changes rather than UAV-specific acoustic signatures. This review asks: (i) what acoustic signatures define UAV noise, how they differ from conventional environmental sounds, and how UAV and operational characteristics shape the emitted source; (ii) how published human-response experiments currently construct and report UAV-noise stimuli and contextual scenes; and (iii) what experimental framework is needed to guide stimulus processing/presentation and protocol/reporting. Synthesising prior evidence, we show that multirotor UAV noise combines operation-dependent tonal blade-passing components and harmonics with broadband content and—critically—exhibits strong nonstationary event structure across flight phases, envelope/modulation dynamics, and directional/motion cues. These features can amplify salience and annoyance beyond what L Aeq /SEL alone capture, while masking and SNR determine which signature cues remain perceptually accessible. We then scoping-reviewed 35 studies using structured coding of stimulus construction, playback/auralization and calibration, SPL/SNR regimes, visual conditions, and outcome measures. The literature is dominated by light multirotor platforms, often simplifying spatial reproduction, and inconsistently reports operational/provenance details (e.g., flight-phase definitions, RPM/payload metadata, independent takes, and masking/SNR treatment), limiting interpretation in UAV-acoustic terms. To close these gaps, we propose a two-part experimental framework: an audio-processing and presentation framework to preserve UAV-defining spectral–temporal–spatial cues, and an experiment-design and reporting framework that specifies minimum information items to improve transparency and reproducibility without implying strict cross-study comparability

Aircraft noise · Background noise · Noisy data · Aerospace and Aviation Technology · Noise Effects and Management · UAV Applications and Optimization

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