Project outline
The rollout of low-carbon technologies is introducing unfamiliar sounds into everyday environments:
- Electric Vehicles (EVs) are equipped with Acoustic Vehicle Alerting Systems (AVAS)
- Air Source Heat Pumps (ASHPs) are becoming a common feature in residential areas as we move away from gas heating
- Wind turbines have transformed rural, highland and coastal soundscapes as renewable energy expands
- Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, are now delivering parcels from the likes of Amazon, Royal Mail, Deliveroo and the NHS
- Electric Vertical Take-off and Landing (eVTOL) aircraft are expected to be deployed in the UK as flying taxis.
These technologies bring clear financial and environmental benefits, yet their acoustic footprints are often quite different from the noise sources they replace. Tonal, fluctuating, high-pitched, and sometimes persistent in character – such sounds can alter how people perceive the quality and tranquillity of their surroundings, and inflict adverse health effects.
The project will combine controlled listening experiments in ambisonics labs and virtual reality settings with psychoacoustic modelling. Advanced data analysis of participant responses and field measurements will be used to develop a psychoacoustic annoyance (PA) model that accounts for interaction effects, ambience and source characteristics. Neural networks will be built to predict the effects of novel noise source exposure and deconstruct complex soundscapes.
The research has three main objectives:
- (1) Better understand the impact of novel sound sources in existing soundacapes;
- (2) Identify the level of influence on annoyance from key factors – ambience, interaction effects & operational contexts;
- (3) Develop tools and models that more accurately model/predict perception of complex soundscapes.
Project Partner
This project is hosted at the University of Salford and supported by DEFRA.
Student
Max Ellis