Max Ellis
Hi, I’m Max! I’m a 2nd Year PhD student from Glasgow, Scotland, now primarily based in South Manchester. I have an audio-focussed academic background in DipHE Music Technology, BDes (Hons) Sound for Moving Image and MSc in Computer Science with Artificial Intelligence. I’m an Associate Lecturer in Computer Science at St Mary’s University, A.I. & Digital Working Group Member in Noise Network Plus, and Committee Member at the Institute of Acoustics Scottish Branch. In my free time I love to play chess and DJ.
Project Title: Psychoacoustic Assessment and Machine Listening-Based Modelling of Novel Noise Source Perception in Complex Soundscapes
Project Partner: Defra
Supervisors: Antonio J Torija Martinez & Zuzanna Podwinska
What is your PhD about?
I’m researching human response to future ambiences and sound environments when novel noise sources are introduced. These new sound sources include drones, electric vehicles (EVs) flying taxis and air source heat pumps. Currently, soundscapes are dominated by ‘broadband noise’ (continuous, distant, uneventful car traffic), but as e-mobility is introduced, the emitted noises are far more eventful and tonal.
Why is it important to do this research?
In the Scottish Highlands and Islands, the NHS and Royal Mail are currently running pilot schemes using drones to deliver medicines and parcels to rural communities. Although drone delivery is cheaper and faster than sending a human courier, drone noise emissions impact wildlife and people. In fact, drone noise impacts attention in school children by up to 40%, causes high blood pressure, impacts sleep and elicits stress and anxiety responses in neurodiverse individuals…now imagine hundreds of commercial and non-commercial drones almost every hour, every day! In addition, there are almost 4 million people in the UK who are visually-impaired, and if EVs cannot be seen, they need to be heard clearly. Fleets of EVs may have non-harmonious consequences or be masked by other sounds. Thus, it is important to consider the perceptions and impact of those sounds on people who will be most affected, e.g. noise sensitive (hyperacusis) or neurodiverse populations.
What drew you to studying this PhD?
I was lucky enough to be given the opportunity to tailor my Masters dissertation into an acoustics project that expanded on previous research at Salford from Dr Marc Green (Arcani Systems). I developed the dissertation further into a conference paper that was presented at Forum Acusticum – Euronoise in Málaga, Spain. That paper has since been expanded into another publication (Lobato et al., 2026) between HEAD Acoustics GmbH and Salford at QuietDrones 2026, Delft. We are continuing to collaborate with HEAD on an upcoming journal paper.
What does a Sustainable Sound Future mean to you?
When the too often forgotten about communities impacted by noise are accounted for: wildlife, pets, children, adolescents, the elderly, rural communities, urban communities, island communities, residents in high-rise flats, residents in underground dwellings, neurodiverse populations – everyone and anyone who is neglected and affected by noise should be at the centre-stage when informing how we challenge noise. That’s what a sustainable sound future should be.
What were you doing before joining the CDT?
Before joining Salford CDT programme, I was studying an MSc in Computer Science with Artificial Intelligence at Abertay University, Dundee. I became interested in AI at the start of 2023 (back when ChatGPT didn’t even have access to the internet!) and wanted to merge my undergraduate studies in sound and audio with machine learning, a niche commonly referred to as ‘machine listening’.
I found out through a friend that some PhDs are actually fully-funded – even paying you an annual stipend – so after seeing the opportunity at Salford, reaching out for a chat with the lead supervisor, having a tour of the facilities, and knowing this opportunity was funded, I knew I had to apply!
What do you do on a typical PhD day?
I have regular meetings with my lead & co-supervisor where we discuss what work I should undertake during the week, and I usually go from there. In Year 1 you are mostly trying to gain a sense of what your PhD is actually going to be through literature reviews, trial-and-error data collection/analysis, sitting in on MSc modules to upskill where required, lots of CDT activities and building some roadmaps or frameworks for your Interim Assessment. In Year 2, the typical day becomes a lot more specific. For me this might be doing signal processing of audio files in Python, building patches for propagating the post-processed audio in MAX/MSP, designing GUI for listening experiments in MATLAB, taking ambisonic recordings and field measurements of different ambiences, labelling your recordings to train machine learning models, and so on.
Tell us a fun acoustic fact!
The sound of a whip cracking comes from the tip travelling faster than the speed of sound creating a mini sonic boom!