Purpose of Review: This review aims to evaluate and discuss the current advances in the measurement and assessment of the noise generated by unconventional aircraft, such as unmanned aircraft systems (UAS) and urban air mobility (UAM) vehicles. Building upon the findings of this review, research gaps are identified, and further work is proposed to enhance existing and emerging methods for the appropriate noise management of these advanced air mobility (AAM) technologies. Recent Findings: Noise has been highlighted as one of the key concerns for the wider deployment of UAS and UAM operations. This is suggested to be due to having acoustic signatures with sound characteristics commonly associated with noise annoyance, such as ‘sharpness’ (the perceived proportion of high-pitched sonic energy) and ‘tonality’ (the perceptual prominence of concentrated sonic energy at discrete frequencies). These types of ‘psychoacoustic features’ are thought to be connected with observations of increased noise annoyance for AAM, compared with conventional aircraft and road vehicles, at the same level of sound exposure. Summary: In the last few years, there has been a growing body of research on UAS and UAM noise. Research has focused on a comprehensive understanding of the sound sources of these unconventional aircraft under a wide range of operating and operational conditions. Based on gathered evidence, measurement protocols for both laboratory and field studies are very advanced for the acoustic characterisation of UAS in terms of sound level, frequency and directivity. Looking at the human response to UAS and UAM noise, loudness has been consistently reported as the main contributor to noise annoyance, with second-order contributions from other psychoacoustic features, such as sharpness, tonality and ‘amplitude modulation’ (fluctuations in loudness over time), varying among studies. Noise targets for UAS certification have been derived from existing regulations for conventional aircraft and rotorcraft, but might not account for the usually reported annoyance offset between UAS/UAM and conventional vehicles. Key research gaps identified include the lack of studies focusing on multiple events, and deeper understanding of the influence that personal or contextual factors may have on responses, which will be important for the development of robust methods for the assessment and minimisation of community noise annoyance due to the operation of these unconventional aircraft. Graphical

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 $L{A}_{max}$ 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.

The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.