Max Ellis
School of Science, Engineering & Environment
Biography
Max is a 2nd Year PhD student in Acoustics and Audio Engineering at the University of Salford’s Acoustics Research Centre, based in Greater Manchester (UK). His research topic is titled 'Psychoacoustic Assessment and Machine Learning-Based Modelling of Novel Noise Source Perception in Complex Soundscape'. The project is funded by the Department for Environmental, Food & Rural Affairs
(Defra) as part of the Sustainable Sound Futures Centre for Doctoral Training. Max's work seeks to:
1. Understand the interaction effects and sound characteristics of novel noise sources.
2. Develop and adapt metrics, methods & models to predict annoyance affect of unconventional future soundscapes.
3. Gather qualitative & quantitative data from UK demographics to assess impact of future soundscapes in context/s.
4. Utilise A.I. and Machine Learning to build tools that can predict human response, with terms for non-acoustic factors (e.g. context).
Max’s academic background spans across several audio and machine learning disciplines. He has achieved a Diploma of Higher Education (DipHE) in Music Technology from University of the West of Scotland and was awarded the BSc Level 8 Court Medal, before transferring to BDes (Hons) Sound for Moving Image at the University of Glasgow. His dissertation focussed on Capabilities of Generative AI Tools for Music Production. He holds an MSc in Computer Science with Artificial Intelligence from Abertay University, with his thesis on ‘Psychoacoustic Annoyance Models and Deep Learning for Prediction of UAV Noise Affect’, which later developed into a conference paper for Forum Acusticum EuroNoise 2025.
He is a Research Member of the Institute of Acoustics (IOA), Committee Member and Early Careers Representative of the IOA Scottish Branch, Associate Member of the Audio Engineering Society, and Noise Network Plus Working Group Member for A.I. and Digital.
Areas of Research
Psychoacoustics, Novel Noise Sources, Perception-Driven Engineering, Machine Listening, Artificial Intelligence (A.I.), Machine Learning (ML), Unmanned Aerial Vehicle (UAV) Noise, Advanced Air Mobility (AAM) Noise, Artificial Vehicle Alert Sounds (AVAS)
Associate Lecturer across 'MSc Computer Science' programme suite modules at St Mary's University Online (SMU Online).
Teaching Assistant on BEng/MSc module 'Environmental Noise, Measurement and Modelling' at the University of Salford
Qualifications
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Doctor of Philosophy (PhD) in Acoustics and Audio Engineering
2025 - 2029 -
Master of Science (M.Sc.) in Computer Science with Artificial Intelligence (Distinction)
2023 - 2024 -
Bachelor of Design (B.Des.) with Honours of the 2nd Class, Upper-Division (2:1) in Sound for Moving Image
2021 - 2023 -
Diploma of Higher Education (DipHE) in Music Technology (with Distiction)
2019 - 2021