Machine learning for audio with applications for health and wellbeing

A PhD to advance the state of the art in machine learning for audio, featuring real-world collaborations with domain experts ranging from healthcare to the entertainment industry.

Start date

1 October 2022


3 years

Application deadline

Funding source

Surrey Institute for People-Centred AI

Funding information

A stipend of £15,609 for 2021/22, which will increase each year in line with the UK Research and Innovation (UKRI) rate, plus Home rate fee allowance of £4,500 (with automatic increase to UKRI rate each year). For exceptional international candidates, there is the possibility of obtaining a scholarship to cover overseas fees.


The studentship’s central theme is Machine Learning for Audio. New AI for sound technology has major potential applications in security, health and wellbeing, environmental sensing, urban living, and the creative sector. Realising the potential of computational analysis of sounds presents particular challenges for machine learning technologies. For example, current research use cases are often unrealistic; modern AI methods, such as deep learning, can produce promising results, but are still poorly understood, and current datasets may have unreliable or missing labels. There is therefore great potential for advancing AI through research contributions in this area.

Solutions you will develop may involve, but not be limited to the following research themes:

  • Bayesian Deep Learning:
    • Monte-Carlo dropout inference
    • HMC (Hamiltonian Monte Carlo)
    • Hybrid approaches
  • Big Data analysis
  • Sound Event Detection
  • Signal Processing

Skills you will develop, which are sought after in further research in academia and industry, include:

  • Time-series analysis
  • Critical literature analysis and implementation
  • Advanced data visualisation and presentation
  • Reproducible research (RR)
  • Open source contributions via GitHub
  • Python programming:
    • Scientific computing in Scipy/Scikit-learn
    • Deep Learning with Keras/Tensorflow 2.0, and/or PyTorch
    • CUDA configuration and high-performance computing
    • (Optional) web communication with Apache/Django 
  • Database languages:
    • MySQL/PostgreSQL
    • MongoDB
  • Unix programming

Example up-and-running projects you may join to rapidly build up your research expertise and portfolio (including publications) include: 

  • As part of CVSSP’s AI4Sound: 
    • The HumBug project with the University of Oxford, Imperial College London, and entomologists worldwide: acoustic mosquito monitoring for malaria vector mapping and intervention 
    • In the Surrey Clinical Research Building: analysing the role of audio and multimodal data on the effect of sleep; using audio to categorise sleep quality, and thus determine biomarkers for disease onset prediction 

Ongoing collaboration with UKHSA and The Alan Turing Institute: machine learning for COVID interventions, including a feasibility assessment of COVID-19 detection from audio, and extensions to other respiratory conditions

Related links

The HumBug Project AI For Sound Turing RSS

Additional notes

Please also see if any areas of research related to the following staff interest you:

Prof. Mark Plumbey

Prof. Derk-Jan Dijk

Eligibility criteria

All applicants should have (or expect to obtain) a first-class degree in a numerate discipline (mathematics, science or engineering) or MSc with Distinction (or 70% average) and a strong interest in pursuing research in AI. A keen interest in signal processing, sound, or general research in audio is highly advantageous. Additional experience which is relevant to the area of research is also advantageous.

English language requirements

IELTS minimum 6.5 overall with 6.0 in Writing, or equivalent.

How to apply

Applications should be made through our Vision Speech and Signal Processing PhD programme page on the "Apply" tab. Please clearly state the studentship title and supervisor on your application.

Vision, Speech and Signal Processing PhD

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Application deadline

Contact details

Ivan Kiskin

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