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Dear List, Closing date 21 April 2024 John Drever and I have won £2.2M from the Leverhulme Trust to fund PhDs in aural diversity. We’re now advertising for our first cohort of six fully-funded PhDs to start in Sept/Oct 2024. Please forward this message to anyone who you think
might be interested. As well as the standard UK stipend of £18,622 per year (+£2k London weighting if at Goldsmiths), we also offer a generous sum of up to £10k per student for research expenses. Typical research expenses include participant recruitment and
conference trips. Aural diversity is the fairly new idea that hearing/listening differences between individuals and groups might be better represented as a spectrum instead of a binary normal/impaired division. The PhD topics will build on the early work
of the https://auraldiversity.org/ research network to apply the aural diversity concept in many disciplines concerned with sound. Hence, the PhDs are mostly rather interdisciplinary and a bit different from mainstream
hearing science. We have a long list of potential PhD topics and supervisors at
LAURA: The Leverhulme Trust Aural Diversity Doctoral Research Hub We aim
to recruit six PhDs this year, and more in the following years, building to a total of 25 researchers in aural diversity. I’m afraid that we can’t accept international applicants this year. I realise this is irritating for a global mailing list. Sorry. The restriction is because the short recruitment cycle in the initial year
will not allow enough time for the UK visa processes. We will have some fully-funded international places available next year (PhDs starting Sept/Oct 2025). Interested applicants should probably look at all the potential topics on our web page, but some that are perhaps a bit closer to the mainstream interests of this list include:
It has been hypothesised that certain musical skills and abilities, such as absolute pitch, benefit from a local cognitive processing style that is commonly found in autistic individuals (e.g. Mottron et al.,
2000; Wenhart & Altenmüller, 2019). However, there is very little empirical evidence yet for the relationship between local vs. global processing styles of music in general and sub-clinical autistic traits. Hence, in a first step this project aims to the answer
the question whether a local vs. global processing style for music is a stable individual trait or just two different listening approaches that can be shifted at will and whether the ability to shift between these depends on the degree of musical expertise.
In a second step, the project aims to clarify whether the degree to which a local processing style for music can be employed to solve a listening task is related to sub-clinical autistic traits (i.e. systematising/empathising traits) of individual listeners.
Supervisor:
Daniel Müllensiefen (Goldsmiths)
Machine learning algorithms are increasing being used to model human listening. In most cases, machine listening algorithms are developed using ground truth data generated with psychoacoustic experiments
on people with ‘normal’ hearing. This results in machine learning algorithms that are then biased against people with diverse hearing. This matters, because these algorithms are then used by other machine learners e.g. to develop better hearing aid processors.
In this project, you will develop machine learning models of attention, with the aim of exploring how to best incorporate more diverse hearing. You will need to be able to program Python within machine learning frameworks and have an interest in psychoacoustics.
Supervisors: Trevor Cox and Sunil
Vadera (Salford)
Some aurally divergent individuals can find many environmental sounds as being disturbing, confusing or even painful to hear. Recent work in AI has improved the capability of sound source separation,
sound (e.g., speech) enhancement and reduction (e.g., background noise). This project would investigate deep learning techniques for sound identification and separation with the aim of facilitating real time rebalancing of environmental sounds based on individual
requirements or needs.
Supervisors: Ben Shirley and Chris
Hughes (Salford)
Current methods for quantifying the impact of noise on exposed communities are based on meta-analyses of exposure-response relationships between noise exposure and annoyance. These assume an average
community responding in a consistent way to environmental noise exposure. We do know that everybody ‘hears differently’ and therefore will respond differently to noise. Because of this, new evidence and novel methods are needed for best practice and regulation
to consider aural and personal diversity in decision-making. This PhD will explore different approaches to incorporate aural and personal (e.g., noise sensitivity) diversity in the assessment and management of environmental noise exposure.
Supervisors: Antonio Torija Martinez and Robert
Bendall (Salford) For further information, including how to apply, please refer to our webpage at
LAURA: The Leverhulme Trust Aural Diversity Doctoral Research Hub We encourage
interested applicants to contact potential supervisors for an informal discussion. For questions which are not about a specific PhD topic, you can email
see-laura@xxxxxxxxxxxxx Cheers, Bill Professor Bill Davies
(he/him) Acoustics Research Centre | School of Science, Engineering & Environment w.davies@xxxxxxxxxxxxx
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