2025 PhD Projects

• Student: Matthew Gray
• Supervisors: Jonathan Hargreaves and Bruno Fazenda

Abstract:
Cultural venues such as concert halls and opera houses provide significant socio-economic benefits to the communities they serve. They are often landmark buildings for a city or region, offering shared spaces for connection and artistic expression, and supporting the creative economy. The success of such venues is determined in part by the quality of their acoustic design, which directly influences performer experience, audience wellbeing, and community engagement—making innovation in auditorium acoustics a matter of both artistic and societal importance. This project, undertaken as part of the CDT in Sustainable Sound Futures (EP/Y034708/1), contributes to the theme of engineering positive sounds to create a better aural future.

The research focuses on coupled volume design approaches for auditoria intended primarily for classical music performance. Based on our experiences and those of orchestras, coupled volumes produce effects on a space’s sound character – both in the frequency and time domains – which are artistically impactful. We aim to define these effects more clearly and robustly, and to understand how they may best be parameterised and designed for. While previous studies have examined this topic, there remains an opportunity to develop a more sophisticated understanding of coupled volume acoustics that extends beyond the traditional focus on energy decay curves and related parameters.

A combination of computational modelling and perceptual evaluation will be used to investigate these phenomena. The outcomes will enable acousticians and designers to create coupled volume spaces with greater sophistication, enhancing the experience for musicians and audiences alike. This project is undertaken in partnership with Arup.

Student: Sarah Armstrong
Supervisors: Trevor Cox & Helen Whitehead

Abstract:
Noise is highlighted by the World Health Organisation (WHO) as the second most harmful environmental pollutant with a growing body of evidence demonstrating its negative impacts on human health. UK policy and guidance prescribes acceptable noise levels, manages noise as a statutory nuisance and regulates noise-intensive activities via licensing. However, there is limited consideration to the effects of noise on wildlife. Non-human animal species produce and perceive sound differently to humans meaning the protections in place may not sufficiently safeguard animals. The literature on non-human sound perception, adaptations to noise and associated fitness impacts is growing, particularly for marine species. In contrast, evidence for the impacts of noise pollution on terrestrial species is more limited, mostly focusing on the alteration of avian call frequency in urban areas. Therefore, there is recognition that greater research into the impacts of noise on ground-dwelling species is required, alongside further investigation into the long-term impacts of noise on birds, beyond known adaptation.

This project aims to develop evidence-based methods for industry to quantify the likely impacts of noise caused by development on UK wildlife for use during planning. This will integrate acoustic data, such as sound pressure levels and spectral analysis, with ecological surveys, behavioural observations and other welfare indicators. This approach will enable developers to identify potential noise producing activities which may have a negative impact on nearby species and implement effective mitigation strategies. Ultimately, this will improve the welfare of local animal communities and contribute to the UK’s wider biodiversity conservation and environmental protection goals. The work is supported by the Department for Environment, Food and Rural Affairs (Defra).

• Student: Rebecca Hutt
• Supervisors: Bill Davies; Graeme Sherriff; David Waddington

Abstract:
Studies investigating annoyance and sleep disturbance have found Neighbour Noise (NN) to be as significant as Road Traffic Noise (RTN). Research is beginning to reveal the health effects associated with NN but lags behind RTN work because of its complex nature: for instance, NN sources have a range of spectral and temporal signatures and are more affected (compared to RTN) by room acoustics and the source-to-receiver transmission path.

Most housing in the UK pre-dates the 1992 Building Regulation Part E for sound separation between dwellings (and equivalent legislation by the devolved administrations); even modern housing is often inadequate as the origins of Part E were not based on NN consideration or optimising privacy. Future new build residents may enjoy higher standards but most of the population will continue to live in older housing with the risk of adverse health effects due to NN. Homes are increasingly our workplaces, so NN research has the potential to support productivity as well as benefit public health.

There are limitations in the evidence from NN studies as most social surveys lack measured noise data, rely on self-reporting and under-report sleep disturbance. Laboratory-controlled listening tests offer objective and subjective evaluation but lack context. Field surveys that include objective evaluation are few and focus on the requirements of new dwellings, not how well existing homes perform.

I plan to use a mixed-methods approach, combining qualitative analysis of lived experiences with quantitative data collection. I will then explore a Hearing Model that offers psychoacoustic parameters (e.g. loudness, tonality, roughness), which may be a better way to describe how well a NN event is tolerated, than an A-weighted sound level. Contextual weightings for duration and number of events will also help to describe perception and risk of sleep disturbance. Along with the physical stimulus of the noise, I will take account of non-acoustic factors (drawing on the ISO/TS 16755-1: 2025 framework) and aural diversity (i.e. beyond “normal” hearing ontology) which the majority of the population live with.

Developing a better understanding of human response to NN will:

(1) help local authorities (and equivalent bodies) to target their resources when investigating cases under statutory nuisance legislation, and

(2) lead towards mapping population exposure to neighbour noise impact, which can be used by public health epidemiologists to calculate adverse health outcomes (as has been done for RTN).

I will also examine the party wall/floor constructions and flanking details of pre-1992 constructions to reveal the influence of housing age/type on NN transmission between dwellings.

The work is supported by the Department for Environment, Food & Rural Affairs (DEFRA).

• Student: Katie Salter
• Supervisors: Antonio J Torija & Simone Graetzer

Air source heat pumps (ASHPs) offer a significant opportunity to decarbonise home heating. However, the increasing rate of installations at homes across the UK and Europe may lead to a rise in community noise, impacting public health and wellbeing, and potentially hindering the adoption of heat pumps. Current standards for assessing noise from ASHPs follow a simplistic approach that does not generally consider the impact of acoustic character (such as tonality and low frequency noise), operating schedule, background sound or the cumulative effects of multiple installations. It is known that these factors affect human perception of sound in general, but the psychoacoustic impact of such factors in the context of ASHP noise is not understood. This project investigates the influence of these factors on community noise. It studies how community noise impacts can be minimised through appropriate ASHP placement and planning, using a combination of fieldwork and laboratory-based psychoacoustic experiments. Ongoing stakeholder engagement throughout the project will ensure the outcomes are actionable and relevant. By improving the way in which heat pump noise is assessed, this project will help to minimise the potential noise issues caused by heat pumps, and will contribute to removing barriers to further heat pump uptake.

• Student: James Hipperson
• Supervisors: Trevor Cox & Jonathan Hargreaves

Abstract:
Numerical methods for acoustic simulations are well established, but scale poorly to high frequencies and large domains. Important applications include simulation of outdoor noise propagation and room acoustics. Physics-informed machine learning is a new and rapidly developing field that has shown promising early results in other physical sciences, and offers significant potential for accelerating and improving accuracy for acoustic simulations.

Key research questions that must be answered in order for this to become a practical methodology for acoustics include:

1. Guaranteeing physically accurate results
2. More efficient training and
3. Scaling and generalisation to unseen problem configurations.

• Student: Oscar Carter
• Supervisor: Trevor Cox

Abstract:
Large numerical simulations dominate marine and aerospace industries when performing structural analysis on engineering platforms. These models are computationally intensive, and model updating often requires complete re-simulation. To enable practical real-time sensing applications for noise and vibration, sufficient sensor coverage is needed to acquire the data which characterises the dynamical behaviour for quantifying both structure-borne noise and radiated air-borne noise.

However, the dynamics and scale of these structures present challenges for physical based measurements, such as satisfying a sufficient spatial resolution for system-wide analysis, real world time constraints and complex multiphysics systems that cannot be easily quantified.

As part of the Sustainable Sound Futures CDT, this proposed research aims to improve the resolution of measurement datasets by using virtual sensors to improve the spatial resolution, improving the ability for future real-time sensing applications to better detect harmful noise and vibration to both people and the wider environment.

Utilising an energy-based system model framework, this research will seek to ascertain the ideal construction of an appropriate Physics Informed Machine Learning (PIML) based virtual sensor, leveraging a data driven approach with physics domain knowledge to compare against traditional virtual sensing approaches.

The predictions will be validated through laboratory experimentation in a hybrid modelling approach and compared to current methods such as Kalman filters and numerical based hybrid modelling techniques for experimental data sets.

• Student: Max W Ellis
• Supervisors: Antonio J Torija Martinez & Zuzanna Podwinska

Abstract:
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; and (3) Develop tools and models that more accurately model/predict perception of complex soundscapes.

• Student: Matt Torjussen
• Supervisors: Antonio J Torija Martinez & Zuzanna Podwinska

Abstract:
This research addresses a critical gap in the evaluation of environmental and technical sounds by developing and validating psychoacoustic models for sharpness and impulsiveness within the Sottek Hearing Model (SHM) framework, then optimising these models for real-world complex soundscapes rather than idealised laboratory conditions.

Current sound evaluation methods rely heavily on basic frequency-weighted and time-weighted sound pressure levels, which inadequately capture nuanced perceptual problems in environmental noise. Whilst psychoacoustic loudness is well-established through international standards, sharpness and impulsiveness lack unified, perceptually validated metrics that account for temporal variations and diverse acoustic environments. Existing standards assume idealised sound field conditions that rarely occur in practice, limiting their applicability to real-world measurements in rooms, vehicles, and outdoor spaces.

The research aims to establish standardised definitions and computational models for sharpness and impulsiveness based on the SHM that accurately reflect human perception across diverse soundscapes.

The methodology combines computational model development with psychoacoustic experimentation through an iterative validation cycle. A comprehensive library of environmental and technical sounds will be compiled, including vehicles, machinery, consumer products, and construction activities. Human perception studies using jury testing will validate model predictions against subjective responses. A second research phase may address sound field optimisation by characterising directional effects using dynamic head-related transfer functions.

The validated models will provide improved assessment tools for urban planning, product design, and environmental policy, supporting the creation of sustainable sound futures by closing the gap between physics and human perception.

• Student: Shahmir Panjwani
• Supervisors: Mahdi Azarpeyvand and Djamel Rezgui

Abstract:
This PhD project will focus on advancing the understanding of the aerodynamic and aeroacoustic characteristics of wing mounted, tractor propellers and investigating how the propeller’s flow field interacts with the wing. The research will involve exploring a range of propeller configurations, flight and operating conditions and examining the influence of blade design on the formation and evolution of the propeller wake over the wing.

• Student: Lucas Lee Zheng Xi
• Supervisors: Mahdi Azarpeyvand and Daniel Poole

Abstract:
The increasing focus on road vehicle efficiency will drive the development of new aerodynamic vehicle shapes, specifically electric cars. Understanding the impact that these future low drag road vehicle shapes have on aeroacoustic noise generation is critical to 1) increasing levels of vehicle refinement in an increasingly competitive sector, and 2) understanding noise pollution from such vehicles which is a core theme of the Sound Futures CDT program. Using a combination of experimental and computational approaches, the project will investigate the impact of lower drag road vehicle shapes on aeroacoustic noise generation, including technologies that are opened-up because of the use of electric power, such as smooth underfloors, automated driving sensors and active aerodynamic devices. The research outcomes will also help inform policy making by incorporating the effects of new vehicle technologies into the broader soundscape and their impact on society. Experimental wind tunnel experiments will be performed using the University of Bristol’s new Boundary Layer Wind Tunnel. These will be complemented by Computational Fluid Dynamic (CFD) simulations to perform detailed analysis and design of future electric cars. This project is supported by (Jaguar Land Rover).

• Student: Ahmed Khan
• Supervisors: Mahdi Azarpeyvand & Dorian Jones

Abstract:
In an increasingly decarbonised world, wind energy offers a clean and renewable energy source. However, noise generated by a wind turbine presents a significant regulatory barrier to mass adoption. In the case of large modern onshore wind turbines, power outputs are commonly capped to meet strict noise regulations aimed at limiting noise exposure to nearby residents. Studies have shown that increasing the noise threshold by 1dB would allow for a 2-3% increase in power output per annum.

The dominant noise source on modern wind turbines is aerodynamic noise (often described as a “whooshing” noise) which this project will focus on. Through thoughtful design, noise sources such as tip vorticity noise can be attenuated out, thus leaving aerofoil self-noise as the remaining dominant noise mechanism. This project will focus on experimentally mitigating this self-noise. The University of Bristol’s aeroacoustic facilities will be heavily utilised over the course of the project to investigate various novel passive treatments (e.g. serrations, brushes, vortex generators, porous materials e.c.t.). At the conclusion of this project, it is hoped that a fundamental understanding of the underlying physics of such mitigations will be made, informing industry, and next generation wind turbine design.

Student: Tilde Resare
Supervisors: Anton Krynkin & Hasan Ghadbeigi

Abstract:
To improve the acoustic performance in indoor environments it is vital to have a good understanding of all possible transmission paths between points in a building. This research focuses on steel stud-based double leafed walls which are building components that act as partitions inside buildings forming separate units or “rooms”. I am specifically focusing on the transmission path through the studs and how the design of double-leafed walls can be improved by the design of the studs.

This project aims to conduct an in-depth analysis of the acoustic performance of steel studs in double-leafed walls. The influence of the stud’s geometry on the acoustic performance of the system of the wall is relatively poorly understood as it is dominated by better understood effects such as the presence of a discontinuity like an air gap or insulative layer. This research will investigate the effect of the stud profile and studs manufacturing process on the acoustic performance of the wall under dynamic loads. The methodology will integrate finite element method (FEM) analysis of various profiles; the investigation of analytical and semi-analytical solutions based on existing shell theory and laboratory experiments to verify the findings. This combined approach is intended to validate the numerical and theoretical findings and to provide recommendations for optimising stud profile to maximise desirable acoustic properties of double-leaf wall systems.

• Student: Thomas Stolarski
• Supervisors: Jon Barker & Simone Graetzer
• (RNID)

Abstract:
Evaluating the effectiveness of hearing aid algorithms can be a challenging task as evaluating different environments and conversational scenarios involves a large amount of preparation and effort on the behalf of both researchers and their participants.

Using virtual reality, we intend to produce a synthetic environment that allows for a balance between reproducibility and controllable variation. Participants wearing a virtual reality headset can participate as a listener in a virtual conversational environment, where the audio they hear is resynthesized in real-time from a ground truth dataset in response to their movements and behaviour within the virtual environment. By applying variations in the algorithms and parameters used for target speaker extraction and audio processing, we can make more reproducible assessments of both the impact of these variations in the experience of individual users, and evaluate which aspects of the transformations vary the most in accordance with user preference.

We expect that the insight gained from this process could better inform hearing aid manufacturers as to what customizations or fitting processes they could offer to their users to better align the hardware and software with their needs.

• Student: Stuart Cumming
• Supervisors: Felix Langfeldt and Giacomo Squicciarini

Abstract:
This project aims to develop virtual models to simulate and assess how humans perceive sound altered by acoustic metamaterials across different applications (such as absorptive treatments and noise mitigation), and to develop an interactive web app that demonstrates the sound of acoustic metamaterials through auralisation.

The emergence of acoustic metamaterials has opened up a wide range of novel ways to control sound, from extremely thin low-frequency sound absorbers to acoustic invisibility cloaks. The performance assessment of current metamaterials-based technologies is exclusively focused on objective metrics, such as absorption coefficients or sound pressure levels. What is missing in the current research landscape is a different view that looks at the human perception of how sound is altered by acoustic metamaterials and whether this can be exploited to make noise sound more pleasant, without necessarily improving objective metrics.

A key property of the models will be the ability to change the properties of the acoustic metamaterial treatments, such as their tuning or the type of metamaterial, to be able to study the sound-changing effects of the metamaterials. Each model will then be validated using the test facilities and equipment at the ISVR. Once validation is complete, virtual models will be used in listening tests to measure the effect of metamaterials on human subjective perception of sound and to contrast these findings with established objective measures.

The novel techniques developed in this project can be used to let the general public listen to the effect of acoustic metamaterials on these sounds through an interactive web app.

• Student: Deokki Min
• Supervisors: Christine Evers and Jonathon Hare

Abstract:
This project aims to develop AI models inspired by the auditory system for improved auditory scene analysis.

While recent advances in machine learning have produced numerous acoustic AI models for various auditory tasks, these models typically adapt architectures from computer vision or natural language processing. This cross-domain borrowing often results in models that lack interpretability and biological plausibility, despite being tuned for acoustic applications.

The auditory system naturally and efficiently performs complex auditory scene analysis tasks. By incorporating prior knowledge from auditory neuroscience—spanning peripheral, subcortical, and cortical processing stages—we can develop AI models that are not only more parameter-efficient but also more explainable and interpretable. These biologically inspired principles have been refined through evolution and are well-documented in auditory research, providing a solid foundation for computational implementation.

This research will systematically integrate insights from different levels of the auditory pathway into deep learning architectures. The methodology includes: (1) reviewing established computational models of auditory processing, (2) designing neural network components that reflect biological mechanisms such as cochlear filtering, temporal modulation processing, and attention-driven segregation, and (3) validating these models on auditory scene analysis benchmarks.

A key application target is embedded robotics, where conventional AI models are often impractical due to their computational demands. Auditory system-inspired models, with their inherent efficiency, could enable robots to perform real-time auditory scene analysis with limited hardware resources, enhancing their environmental awareness and interaction capabilities.

• Student: Jack Hallybone
• Supervisors: Stefan Bleeck, David Simpson and Yasmeen Hamza

Abstract:
This project aims to develop our understanding of how noise impacts cognitive functioning, for example as measured by the change in a listener’s ability to perform a task in the presence of noise. Of particular interest is how the acoustic properties of noise impact our everyday thinking and productivity by consuming sensory and cognitive resources, and how hearing technology – like hearing aids and earbuds – can reduce this impact and recover cognitive performance.

Through research into cognition and attention the impact of sound on various auditory and non-auditory measures of core cognitive functioning has been well studied. There is also now a growing body of literature indicating that hearing aids, and the signal processing techniques they employ, can improve performance. By using both constructed and ecologically valid noise, the literature begins to identify what acoustic and semantic features in a sound produce these effects.

Using a mixture of behavioural and physiological measures to assess the change in participants’ cognitive load and performance, the objectives of this project are to further understand: (1) what makes a noise “cognitively challenging”; (2) how this impacts real-world tasks such as communicating or learning in noise; and (3) how this knowledge can be used to assess a hearing technology for its impact on cognition.

The effort involved in listening is a common complaint for hearing aid users. This project addresses the central aim of the Sustainable Sound Futures CDT program – to create a better sounding future – by helping to support the design of hearing technology with a focus on reducing the cognitive impact of noise, which in turn may reduce effort and fatigue and address the issues of dissatisfaction and social withdrawal.