Funded PhD Opportunities

Application Deadline – 7 April 2025

• Project type: Industry-driven
• Supervisors: 1) Antonio Torija Martinez and 2) Zuzanna Podwinska, University of Salford
• Project Partner: HEAD Acoustics

You will focus on one of the following:

1. Development of psychoacoustic models for sharpness and impulsiveness

Sound quality metrics are often used to analyse complex sound scenarios, e.g. for soundscape applications. Sound quality can also affect the health and well-being of people in a given environment. Therefore, it is of the utmost importance that the definition of good sound quality in a given context is as precise as possible. In this regard, psychoacoustic indicators are usually used to develop these metrics.

In recent years, several psychoacoustic standards have been published based on the Sottek Hearing Model: the SHM Loudness, a new approach to time-varying loudness based on a nonlinear combination of partial tonal and noise loudness (as part of the SHM Tonality, standardised in ECMA 418-2) to better account for the fact that the loudness of tonal components, i.e. tonal loudness, may have a stronger influence on loudness perception than the loudness caused by the other components, i.e. noise loudness. In addition, there are standards for psychoacoustic modulation analysis: the SHM Roughness for the assessment of fast modulated sounds (standardised in ECMA 418-2) and the SHM Fluctuation Strength, an adapted model for slow modulated sounds (to be standardised in 2024).

Other very important psychoacoustic parameters are sharpness and impulsiveness. Existing sharpness models have some drawbacks: for example, they do not adequately account for loudness and temporal effects, and they do not provide sharpness values that linearly correspond to human perception. In addition, there is only a German standard for stationary sounds. For impulsiveness there is currently no standard, but a model based on the Sottek Hearing Model published in 1993.

You will develop improved models for sharpness and impulsiveness based on the recently standardised Sottek Hearing Model.

2. Evaluation of complex acoustic environments using sound source separation methods:

Acoustic environments often consist of a multitude of different individual sound sources with varying acoustic qualities. Despite this, groups of participants can reliably reach a consensus regarding the most dominant sound sources and the most important qualities of such environments.

When perceiving an acoustic environment, humans intuitively separate different sound sources using both ear signals. Previous studies have shown that sound sources with a clearly perceived direction are often judged differently from non-directional sources. For example, sound sources with annoying qualities are rated as even more annoying when they have a clear direction. Therefore, identifying dominant sources and their directions from a binaural recording can help to better assess the perceived qualities of a given acoustic environment.

Objectives:

• Algorithmic identification of dominant sources and their direction from binaural recordings.
• Separation of dominant sources based on direction for individual auralisation and analysis.
• Extension of both approaches to multi-channel spatial audio (Ambisonics) recordings.

Either topic would suit graduates with a solid understanding and expertise in signal processing and coding. Experience in statistics (or machine learning) and jury testing will be useful.

You will be based in the Acoustics Research Centre at the University of Salford

Application deadline: 23 April 2025

• Project type: Industry driven
• Supervisor: David Waddington d.c.waddington@salford.ac.uk
• Project Partner: Defra

Join a world-leading acoustics research team at the University of Salford’s renowned Acoustics Research Centre, working within the prestigious Sound Futures Centre for Doctoral Training (CDT). This full-time, fully-funded 3-year PhD studentship offers a unique opportunity to develop innovative methodologies for quantifying and assessing anthropogenic noise impacts on wildlife populations. Your research will address critical knowledge gaps in environmental acoustics and contribute directly to national and international conservation efforts and environmental policy development.

Working at the intersection of acoustics, ecology, and environmental science, you will develop robust, evidence-based assessment frameworks applicable across regulatory contexts. This project is supported by the Environment Agency and Defra, with representatives serving on the Advisory Board. The research will focus on developing tools applicable to a wide variety of species, aligning with the Habitats requirements of these governmental bodies.

Anthropogenic noise from urbanization, transportation, and industrial activities represents a significant yet often overlooked environmental stressor affecting wildlife behaviour, communication, and survival. This PhD project will develop a novel assessment framework to standardize methodologies for evaluating noise impacts across diverse ecosystems and species.

Objectives

• Critically review and analyse existing UK legislation and guidance on noise pollution and its impacts on wildlife, including relevant Environmental Protection Act provisions.
• Develop a comprehensive assessment framework that integrates acoustic measurements, species responses, and environmental factors to evaluate noise impacts across multiple taxa.
• Collaborate with the Environment Agency and Defra to ensure the developed tools meet regulatory requirements and can be applied in environmental impact assessments.

Applicants will have access to Salford’s comprehensive suite of acoustic measurement equipment, anechoic and reverberant test chambers, and bioacoustic monitoring technologies.

Expected Outcomes

The development of a robust and standardized framework for evaluating noise pollution impacts on wildlife across multiple species. This framework will support environmental regulators in making informed decisions regarding habitat impact assessments and project approvals, ensuring compliance with UK legal requirements and promoting biodiversity conservation.

Requirements

• A first-class or 2:1 honours degree or a Master’s degree in Environmental Science, Ecology, Acoustics, or a related field.
• Strong analytical and quantitative research skills. 
• Excellent written and verbal communication skills, with the ability to present complex information clearly and concisely. 

Application Deadline – 7 April 2025

• Supervisor: Professor Jon Barker, University of Sheffield and Dr Simone Graetzer, University of Salford
• Project Partner: The Royal National Institute for Deaf People (RNID)

Hearing loss affects over 5% of the world’s population, making it a major public health concern. Hearing aids are the most commonly prescribed treatment, but many users report they do not perform well for listening to speech in noisy situations. Breakthroughs in deep learning and low-power chip design are driving the next generation of hearing devices and wearables, with the potential to revolutionize speech understanding in challenging listening environments. For example, Apple’s AirPods Pro have gained FDA approval as hearing aids for mild to moderate hearing loss, and Phonak has introduced deep neural network-equipped devices that dynamically enhance speech clarity in noisy environments. However, training these approaches to work in general settings and to suit individual preferences remains a critical challenge.

To improve deep learning-enhanced hearing aids, we require metrics that predict how well a given hearing aid algorithm will perform for a specific user in a particular acoustic environment. Existing approaches often rely on oversimplified assumptions about listener preferences, which are captured using basic metrics. For example, it is often assumed there is a well-defined target speaker and that processing should maximise noise suppression while preserving quality. These simple metrics do little to capture users’ needs in more complex settings, such as trying to engage in multiparty conversations in a busy restaurant.

The project will explore a variety of methods to understand hearing device user preferences in more complex settings, including leveraging virtual reality (VR) to simulate diverse acoustic environments and hearing aid algorithms. VR offers the advantage of creating immersive and controlled scenarios where users can directly experience and evaluate different algorithmic configurations. This approach allows the systematic measurement of user preferences across a wide range of conditions, ensuring both ecological validity and experimental rigor. From this understanding new algorithm quality metrics will be derived for optimising existing deep-learning enhancement approaches in a more user-dependent manner.

The project will be based at the University of Sheffield and co-supervised by experts from both Sheffield and the University of Salford, collaborators on the ongoing EPSRC-funded Clarity Project . The Clarity Project focuses on improving speech-in-noise understanding, making it a natural foundation for this work. The Royal National Institute for Deaf People (RNID) will act as a key partner, offering additional expertise and a crucial end-user perspective.

Application deadline: 25 April 2025

• Project type: Academic-led
• Supervisors: Sifeng Bi, Daniil Yurchenko

Vibroacoustic modeling is crucial in engineering systems for accurately predicting, monitoring, and controlling noise and vibration, especially in aerospace and automotive applications. Virtual decoupling techniques have been successfully applied to isolate and analyze vibroacoustic systems in low-frequency domain [1]. However, uncertainties often limit the model’s accuracy. Furthermore, vibroacoustic systems pose challenges due to the absence of a single analysis technique suitable for all frequency ranges [2]. Different physical behaviors and uncertainty impacts in low-, mid-, and high-frequency domains necessitate distinct approaches. Stochastic model updating methods combined with uncertainty quantification provide a systematic framework to address these issues and enhance the reliability of vibroacoustic models [3]. This project aims to integrate these methods, developing a comprehensive framework for the calibration, verification, and validation (V&V) of vibroacoustic models under uncertainty.

1. Bi, S., Ouisse, M., Foltête, E., & Jund, A. (2017). Virtual decoupling of vibroacoustical systems. Journal of Sound and Vibration, 401, 169–189.
2. Sestieri, A., & Carcaterra, A. (2013). Vibroacoustic: The challenges of a mission impossible? Mechanical Systems and Signal Processing, 34(1–2), 1–18.
3. Bi, S., Beer, M., Cogan, S., & Mottershead, J. (2023). Stochastic Model Updating with Uncertainty Quantification: An Overview and Tutorial. Mechanical Systems and Signal Processing, 204, 110784.

Objectives:

The objective is to develop a data-driven framework for vibroacoustic modeling across different frequency domains. This framework will integrate suitable combinations of techniques for low, mid, and high frequencies and incorporate dedicated model calibration and V&V approaches tailored to the specific features and uncertainty challenges of each frequency domain.

Proposed Work Packages:

WP1: Uncertainty Quantification (UQ) for Vibroacoustic Modelling

Tailor UQ techniques for each frequency domain: FEM and Modal Analysis for low frequencies, FEM-SEA and WFEM for mid frequencies, and SEA, Ray Tracing, and Geometrical Acoustics for high frequencies, addressing uncertainties specific to each domain.

WP2: Model Updating for Different Frequency Domains

Develop and apply model updating techniques for vibroacoustic systems across frequency domains, improving accuracy by incorporating experimental data and adjusting for uncertainties in each range.

WP3: Data-driven Approach for Efficiency and Applicability

Use data-driven techniques to enhance computational efficiency, reducing model complexity and making the framework more practical for real-world applications, especially in real-time settings.

WP4: Verification and Validation (V&V) Implement a rigorous V&V process by comparing model predictions with experimental data, ensuring accuracy across all frequency domains, and addressing specific uncertainty challenges for each range.

Application deadline: 25 April 2025

• Academic-led project
• Supervisors: Jen Muggleton and Phil Joseph
• Project partner: UK Water Industry Research

Leakage from pipes is a major issue in the water, gas and oil industries, not only in environmental terms, because of wasting an important natural resource, but also in economic and health terms. In particular, UK Water Industry Research (UKWIR) have identified leakage as one of their strategic priorities and have recently set up the Zero Leakage 2050 initiative, aiming to address the question “How will we achieve zero leakagein a sustainable way by 2050 ?”. The acoustic signals generated by a leak and thence propagated along pipelines have been exploited for many decades to locate leaks but, historically, by making measurements directly on the pipes.

This exciting and innovative project aims to develop an acoustic geo-camera system using two (or more) on- or above-ground sensors to carry out synchronised multiple ground vibration measurements to locate a leak and/or a pipe system.

The project will be a balance of theoretical and experimental work, with some of the experimental work being undertaken at outdoor test sites in the UK, provided by the industrial partner; it would suit a candidate with an interest in signal processing, strong analytical/numerical skills and an enthusiasm for experimental measurements. Specifically, the following will be investigated:

• Wave propagation modelling (within the pipe and in the ground), both analytical and (possibly) numerical
• Development of signal processing methods, including beam forming techniques to combine sensor outputs
• Inverse methods to relate the surface response to the in-pipe behaviour
• Experimental measurements

Application deadline: 25 April 2025

• Academic-led
• Supervisors: Rafael Mestre, Mohammad Belal, Jen Muggleton

This PhD project aims to develop machine learning (ML) and signal processing techniques for analysing data from Distributed Acoustic Sensing (DAS) systems in urban environments. DAS technology, which uses fibre optic cables to detect acoustic signals over large distances and with extreme temporal, frequency and spatial sensitivity, offers real-time monitoring for applications such as traffic flow, pipeline integrity, and noise pollution detection. The primary challenge is efficiently processing the vast amount of data generated by DAS, especially in noisy and complex urban settings.

The student will develop novel ML models combined with classical signal processing techniques to detect and classify key events such as pipeline leaks, traffic congestion, or sound pollution using this acoustic data at different frequencies. These models will be designed to handle noisy data and operate in real-time, making them suitable for urban monitoring systems. The specific application of DAS, whether focused on traffic, infrastructure, or environmental monitoring, will remain flexible and open for the student to shape based on their research interests and the needs of the project. This project will be embedded in an ongoing grant that provides access to DAS data collected from urban environments in Southampton and London, allowing the student to test and refine the developed models in real-world scenarios. Additionally, the student will have the opportunity to engage in interdisciplinary collaboration with researchers working in fields from the social sciences to the humanities. This will provide a broader perspective on how DAS technology can be applied across various sectors, enabling the student to explore diverse applications and work in a multidisciplinary research environment.

Application deadline: 25 April 2025

• Academic-led project
• Supervisors: Steven Bell and Stefan Bleeck

There is a large cost in terms of staff time and devices in supplying hearing aid provision. Automation and machine learning, for example in terms of automated hearing testing, machine learning to seek information from patients and self-fitting hearing aids have the potential to reduce the staff time required to deliver services and hence to improve access to services. However it is critical to still be responsive to the needs to service users and to be safe.

The aim of this project is to explore which aspects of hearing assessment and hearing aid provision for adults in the UK might be automated. It would develop an understanding of the current care pathway in the UK, identify aspects that might be automated and then explore how that implementation could occur whilst maintaining a high-quality service for the end user. It is also important that any approaches used are considered trustworthy. The intended end point is a set of recommendations for clinical practice with some evidence demonstrating the clinical efficiency of such approaches.

Application deadline: 25 April 2025

• Project type: partner-led
• Supervisors: Bahareh Zaghari and Giacomo Squicciarini
• Project partner: iNetic

Addressing the noise and vibration challenges posed by new sound sources in electrified drivelines is a fundamental step in the sustainability of transportation. By minimising noise and vibration while optimising weight, size, and maintenance costs, one can create quieter and more efficient vehicles. This not only improves user comfort but also reduces carbon and non-carbon emissions and mitigates environmental noise pollution. These advancements make cleaner transportation more affordable and accessible to a broader segment of the population, contributing to a more sustainable future.

In an integrated driveline system comprising an energy source, electrical machine, inverter, and gearbox, noise and vibration can originate from various sources, including electromagnetic forces, mechanical imbalances, and high-frequency switching. These components, when coupled together, can amplify vibrations and noise. For example, electromagnetic noise from the motor can interact with gear meshing vibrations, while high-frequency switching noise from the inverter can propagate through the structure, causing complex vibrational patterns. The integration challenges include managing structural resonances, mitigating torque ripple, and ensuring that vibrations from one component do not negatively impact the performance or safety of others.

To effectively mitigate noise and vibration in such systems, the student will develop a holistic modelling approach that balances total weight and size of the system, performance and efficiency, with noise control strategies. This includes vibration isolation sound insulation, and changes to the design of the parts.  The student will also characterise the mechanical and electrical behaviour of the different components in isolation and when coupled to a  host structure and develop algorithms to evaluate and mitigate the vibration response. After the modelling stage, the selected designs of fully integrated driveline, will be built and tested at iNetic.

Application deadline: 25 April 2025

• Academic led
• Supervisors: Michal Kalkowski and Tiina Roose

Sound plays a role in plant ecology, but the understanding of its significance is still in its infancy. Recent results suggest that plants emit sounds indicating their condition and react to sounds in their vicinity (e.g. pollinators). However, most studies on the topic are mere observations focusing on biological processes. This project aspires to advance plant acoustics by offering a solid physics-based foundation for our understanding f plants’ sensory and transmission capabilities. The project involves developing vibration/wave propagation models to understand plant dynamics in response to vibroacoustic stimuli across broad frequency ranges. It builds on recent biological research suggesting plants’ biochemical response to incoming signals, and acoustic emissions. The candidate will integrate theoretical modelling with measurements to analyse plant behaviour. They will also work on determining fundamental acoustic and vibration models to identify features responsible for receiving signals with given properties (e.g. frequency bandwidth). These efforts will be supplemented by the analysis of wave propagation in plant material and coupling with the surrounding medium to study energy transfer.

This project hopes to find applications in environmental monitoring, agriculture and biological sciences. This interdisciplinary study requires a candidate with a background in engineering, applied physics, acoustics, or a related field. The role includes model development, vibration/wave measurements, and collaboration with experts in related disciplines. The project offers the opportunity to contribute to a novel area of research, bridging physics and biology.

Application deadline: 25 April 2025

• Academic-led project
• Supervisor: Matthew Wright
• Project partner: The OHMI Trust, a charity that supports disabled musicians

Being able to play a musical instrument is an important gateway to artistic and personal self-expression, and to participation in social activities. Upper-limb differences and disabilities can present a barrier to all these desirable outcomes. Organisations such as OHMI do valuable work to develop solutions such as adapted instruments, but these are often expensive because they are produced in small quantities. The most successful accessible instrument would appeal to all players, disabled or not.

Most wind instruments work by the player covering fingerholes, pressing valves etc, to change the input acoustic admittance seen by the mouthpiece. The peaks and troughs in its admittance function determine the frequency that the mouthpiece (fipple, reed, lip-mouthpiece combination etc) oscillates at and hence the note that the instrument plays.

This project is to develop a class of programmable-impedance wind Instruments, which would use active control of the waves in the air column to control its input impedance via electroacoustic actuators and sensors in the pipe. This could be attached to a regular mouthpiece and blown expressively as normal, but the note-selection process would be DSP-controlled and the input from the player could come via a range of possible interfaces: MIDI keyboard, touch-sensitive pad, etc.

This would allow a disabled musician who can’t, for instance, cover fingerholes to play such an instrument, while retaining expressiveness (unlike conventional digital wind instruments). It would also allow musicians to extend the capabilities of their instrument by allowing effects such as vibrato, by dynamically changing the impedance curve to affect the pitch or tone of the note, meaning its appeal would be wider than just those who need accessible instruments.

Application deadline: 25 April 2025

• Project Type: Academic Led
• Supervisor: Stefan Bleeck

This PhD project aims to investigate how existing and novel speech enhancement technology can be utilized to enhance cognitive function in individuals with mild to moderate hearing loss. Mild to moderate hearing loss affects a significant portion of the population, often going undiagnosed and untreated, and can lead to challenges in noisy environments. These challenges result in increased listening effort, cognitive fatigue, and reduced productivity. This project will explore how speech enhancement technologies, such as standard hearing aids and wearable devices like AirPods, can be used to reduce listening effort and potentially improve cognitive abilities.

The research will involve:

• Evaluating the effectiveness of speech enhancement technologies.
• Assessing the impact of these technologies on speech intelligibility, cognitive function and neural activity using physiological measures (e.g., EEG, pupillometry).
• Investigating the real-world benefits of speech enhancement in diverse listening environments.

The project offers the opportunity to:

• Contribute to a project with significant potential for societal impact, including improved productivity, work performance, social participation, and overall quality of life for individuals with mild to moderate hearing loss.
• Conduct impactful research at a world-leading institution and work with cutting-edge methodologies.
• Critically, this collaboration will provide access to expertise in areas such as auditory modeling, advanced signal processing, and machine learning for audio.

Application deadline: 25 April 2025

• Project type: Academic led
• Supervisors: Daniil Yurchenko and David Toal

The development of innovative dynamical systems and materials is crucial for advancements in engineering, robotics, and materials science. Traditional methods rely on predefined configurations and human intuition, but recent breakthroughs in generative AI offer new possibilities. By leveraging AI-driven design, we can explore a vast space of potential system configurations, leading to novel structures with optimised properties. This project aims to utilise generative AI techniques to create multi-degree-of-freedom and distributed systems with new and advanced acoustical and vibration properties like enhanced damping, energy absorption, or wave propagation characteristics. These systems will play a fundamental role in mechanical, structural, aerospace, automotive and power engineering, with applications ranging from vibration control to meta-materials with tailored properties. Generative AI has demonstrated remarkable capabilities in solving complex problems, as seen in AlphaGo’s and Alpha Zero’s victories over human grandmasters. By applying AI models to the automated generation and evaluation of existing systems, we can efficiently search large design spaces that would be impractical to explore manually. Machine learning algorithms, including reinforcement learning and deep generative models, will predict optimal configurations based on predefined performance criteria. 

We seek a highly motivated PhD candidate with a background in computer science, applied mathematics, or engineering. The ideal candidate should have excellent programming skills, experience with machine learning frameworks and optimisation.