Modelling the Noise Impacts of Advanced Air Mobility

With the RefMap Project concluding on 30/1/2026, the following post was written to provide an overview of the work of the Acoustics Research Centre of the University of Salford and their results in the project.

With drones and advanced air mobility (AAM) aircraft poised to take to the skies over our cities in ever greater numbers, one key question is: what is this going to sound like? What will adding the high-pitched whir of drones and other forms of Urban Air Mobility do to our soundscapes and annoyance levels?

Within the EU-funded RefMap project, the Acoustics Research Centre of the University of Salford (USAL) has led key research to address this question. As the leader of Work Package 4 (Noise Modelling and Perception), USAL's team was tasked with advancing our understanding of how humans and wildlife alike perceive and react to AAM noise.

Here is a look at their role and the results they have delivered.

From Decibels to Annoyance

While traditional aviation monitors noise primarily based on sound energy, USAL’s approach is rooted in psychoacoustics, i.e. how we perceive sound in a holistic way, accounting for different sound qualities such as volume, pitch and tonality.

As Prof. Antonio J Torija Martinez, leader of the RefMap noise research at Salford, notes, the focus here is not just on loudness, but what is actually perceived by people on the ground which depends on multiple factors.

“So, for example, if you have a drone flying overhead, then you have the noise emitted, you have the noise reaching the ground, and also you have the noise already there in that environment,” he explains. “What we have done is create models that allow us to estimate the annoyance due to the drone's operations in a given environment… We have developed models that allow us to estimate how annoyance levels will change when drones are operating - the delta annoyance. And we have also developed models to estimate the percentage of highly annoyed people in an environment where drones are operating.

“The outcomes of our research can be used to define public acceptance targets for noise in urban areas, thus informing future regulation, policy or guidance development concerning drone noise impact management and public engagement.”

To achieve this, the team has conducted extensive listening experiments in their laboratories and during soundwalks in real-world locations ranging from Manchester to Athens and the Isles of Scilly.

Key Findings: Context is key

Salford’s research has produced several important insights:

1. The "Masking" Effect 

One of the most significant findings is that the impact of a drone depends heavily on what is already happening on the ground. Research published in the Journal of the Acoustical Society of America revealed that drone noise causes a significantly steeper increase in annoyance in calm urban parks compared to busy city streets. In busy areas, existing traffic noise masks the drone; in quiet zones, the drone stands out sharply [1][2].

2. It’s Not Just Volume—It’s Character 

Salford’s researchers found that annoyance isn't driven solely by loudness. Specific sound qualities – particularly sharpness (high pitch) and tonality (whining or buzzing sounds) – are critical predictors of how annoying a drone feels to people on the ground. They also discovered that the unsteadiness of the sound (how much it fluctuates due to wind or maneuvers) makes it significantly more annoying. This is part of the reason a drone can be perceived as more annoying than a conventional aircraft at similar loudness levels.

3. Quantifying "Detectability"

The team has successfully developed and verified models for detectability, a metric that predicts how likely a person is to notice a drone within a specific soundscape. This metric has proven to be a strong predictor of changes in annoyance in urban environments.

Protecting Wildlife

Salford’s role extends beyond human comfort. As part of its work on RefMap, the team is also developing a framework to assess the impact of drone noise on wildlife, with an initial proof of concept focusing on birds. This involves reviewing how noise affects avian behavior and conducting field surveys.

Optimising trajectories based on noise

The models developed by Salford form a key component of the broader RefMap analytics platform. Prof. Torija Martinez highlights that these tools allow decision-makers to make complex trade-offs. 

"A decision-maker could optimize the trajectory based on 'delta annoyance,' which means you might fly the drone over noisy environments because the change is going to be lower there. But we can also give you the percentage of highly annoyed people – the absolute value. In that case, a decision-maker might decide to avoid noisier areas because of the greater number of annoyed people there. We are offering the tools to decision-makers so they can optimize trajectories based on either parameter. We provide the tools; they decide the strategy."

By feeding these psychoacoustic models into trajectory optimization tools RefMap is helping ensure that noise impacts are properly taken into account when planning the flight paths of the future.

See the RefMap Noise Tool

References

[1] Lotinga, M. J. B., Green, M. C. and Torija, A. J. (2025). "Development of psychoacoustic prediction models for short-term noise annoyance responses to unmanned aircraft systems." Journal of the Acoustical Society of America 158(3): 2062–2082. DOI: 10.1121/10.0039056.

[2] Green, M. C., Lotinga, M. J. B. and Torija, A. J. (2025). "Shaping future soundscapes: Affective impact of unmanned aircraft systems noise in urban environments." Journal of the Acoustical Society of America 158(4): 2763–2778. DOI: 10.1121/10.0039523.