PhD Studentship: Sounds Asleep - Towards better sleep quality with biometric noise metering and mediation

Noise can have a significant impact on health and wellbeing, including cardiovascular health, sleep disruption, working cognition, and cumulative hearing damage. Many people in the UK describe their environment as excessively noisy, a significant proportion of the UK population is dissatisfied with their auditory environment (~30%) and sleep disruption in the home environment has been shown to affect both mental and physical health. Noise measures are not readily interpreted by non-specialist audiences, and therefore tend not to encourage proactive adoption by the public. New metrics may help to empower people dealing with noise and interrupted sleep. Advances in affordable, comfortable biophysiological sensors suggest a unique opportunity to explore the relationship between existing acoustic measurement, self-reported responses such as annoyance, and biophysiological measures, creating new, consolidated metrics for noise measurement.

This project will also represent the first step towards the possibility of using biofeedback to facilitate noise masking using automated soundscape generation. Individually-adaptive soundscape masking may be effective in mediating the impact of noise - the user would optimally remain in a quality state of sleep while the system responds autonomously. Machine learning can provide a way to use complex combinations of biofeedback as useful control signals for soundscape generation. Such techniques require a large amount of data and mass market wearables are now reaching a level of maturity which can facilitate such experiments.

This project is a collaboration between us and the Department of Electronic Engineering AudioLab at the University of York. The successful candidate will be supervised by Professor Damian Murphy in the AudioLab and co-supervised by Dr Duncan Williams from Digital Creativity Labs and the student will spend time placed within Digital Creativity Labs as part of their research.

The project will apply both research and development, as well as practice-based approaches and user studies. The successful applicant should have a strong interest in acoustics, sound and music technology, excellent programming skills (e.g. Python, Java, MATLAB or equivalent) and an understanding of DSP and sound design practice. Some background in Machine Learning would also be an advantage.

Funding:

This EPSRC DTP funded studentship will cover the tuition fee at the home/EU rate (£4,260 in 2018/19) and a stipend at the standard research council rate for a period of 3 years (£14,777 in 2018/19). Full details on student eligibility can be found on the EPSRC website.

This PhD is due to start 1 October 2018.

If you are interested in applying for this studentship and would like to know more about the project, please contact Damian Murphy in the first instance.

Entry requirements:

Candidates must have (or expect to obtain) a minimum of a UK upper second-class honours degree (2.1) or equivalent in Computer Science, Electronic Engineering, Music Technology or a related subject. Prior research or industry experience would also be an advantage.

How to apply:

Applicants must apply via the University's online application system. Please read the application guidance first so that you understand the various steps in the application process. To apply, please select the link for the PhD in Electronic Engineering or PhD in Music Technology for October 2018 entry.  Please specify in your PhD application that you would like to be considered for this studentship.  Applications will be considered after the closing deadline of 31 August 2018.

Closing Date: 31 August 2018