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  Computational Acoustics: Predicting, Simulating and/or Auralising how the world sounds

   School of Science, Engineering and Environment

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Information on this PhD research area can be found further down this page under the details about the Widening Participation Scholarship given immediately below.

Applications for this PhD research are welcomed from anyone worldwide but there is an opportunity for UK candidates (or eligible for UK fees) to apply for a widening participation scholarship.

Widening Participation Scholarship: Any UK candidates (or eligible for UK fees) is invited to apply. Our scholarships seek to increase participation from groups currently under-represented within research. A priority will be given to students that meet the widening participation criteria and to graduates of the University of Salford. For more information about widening participation, follow this link: [Scroll down the page until you reach the heading “PhD widening participation scholarships”.] Please note: we accept applications all year but the deadline for applying for the widening participation scholarships in 2024 is 28th March 2024. All candidates who wish to apply for the MPhil or PhD widening participation scholarship will first need to apply for and be accepted onto a research degree programme. As long as you have submitted your completed application for September/October 2024 intake by 28 February 2024 and you qualify for UK fees, you will be sent a very short scholarship application. This form must be returned by 28 March 2024. Applications received after this date must either wait until the next round or opt for the self-funded PhD route.


Project description: This PhD is about development of novel computational algorithms for simulating acoustic wave physics (sound). Such prediction models are at the heart of modern acoustic engineering and are used in a diverse range of applications from refining the acoustic design of classrooms and concert halls to predicting how noise exposure varies through an urban environment. A notable application is Auralisation, which is an extremely important application of computational acoustics. This is the process of rendering the acoustic features of an environment to be heard by a human listener, allowing you to experience the sound of buildings and spaces before they are built or long after they are lost (e.g. ancient amphitheatres). It attempts to reproduce exactly what you would hear if you were really there, so sounds should appear to originate from different locations with all the timbral changes, echoes and reverberation you would experience in the real space. This is a revolutionary technology – and is tightly integrated with virtual reality and augmented reality – but I places unprecedentedly high demands on the computational acoustics algorithms that generate the data it presents to listeners.

Current computational acoustics approaches are well-established but limited. Notably, auralisation of a space requires measured or simulated data covering the full audible frequency spectrum. For numerical simulation this is extremely challenging, since that bandwidth covers many octaves in which the wavelength changes from being large with respect to features of the space to being comparatively much smaller. Hence the most efficient way of describing acoustic propagation changes from wave descriptions at low frequencies to geometric ray and sound-beam energy descriptions at high frequencies, and these differences are reflected in the disparate classes of algorithms that are applied. Geometric propagation assumptions yield efficient algorithms, but the maximum accuracy they can achieve is limited by how well the geometric assumption represents sound propagation in a given scenario; this comprises their accuracy at low frequencies in particular. Methods that directly model wave effects are more accurate but have a computational cost that scales with problem size and frequency, thereby limiting them to small or low frequency scenarios. Hence it is often necessary to operate two algorithms in parallel handling the different bandwidths. Due to their differing formulations however, combing the output data can be a rather arbitrary process.

This PhD project will pursue solutions to these shortcomings with the long-term aim of creating a single unified full-bandwidth algorithm for early-time. You might choose a project brief that focuses on a limited aspect of this, e.g., a specific algorithm, or more holistic questions of how combined data can be auralised. Either way, if you are interested in pushing the state of the art in this area and feel you have the technical / mathematical / coding ability to succeed then I am interested to hear from you. It is an exciting area and there is much that can be done.

Computer Science (8) Engineering (12) Mathematics (25) Physics (29)

Funding Notes

This PhD project is currently only available to self-funded students, or students able to secure funding to complete their studies.


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