I am a PhD researcher at Queen Mary University of London. I work within the sound synthesis research team, where I focus on perceptual evaluation of synthesised sound effects. My research evaluates current state of the art in sound synthesis and aims to objectively identify what makes a particular sound effect realistic.
My research interests include Sound synthesis, procedural audio, audio production technology, real time and live mixing tools and DSP. Perceptive, qualitative and objective measures and metrics for the evaluation of audio technologies.
I received my MSc from Queen Marys University of London in 2014, where my master project “Microphone Bleed Reduction and Dereverberation” was undertaken as part of the Audio Engineering research group within the Center for Digital Music. During this time I became involved in a number of projects including
- Applications of Dereverberation in assisting with the live sound microphone bleed and blind source separation problem.
- Embedded DSP development – Developing a real-time audio system processor with hardware control on a Beagle Bone Black.
- VST Plugin Development using the JUCE framework
- A study on correlations between speech intonation and musical lyric melody
- Open Source audio effect implementation for an embedded Arm Cortex M4 Processor – The Hoxton Owl Guitar Pedal
I grew up Edinburgh and graduated from Edinburgh University with a BSc in Artificial Intelligence and Computer Science in 2011. During my time at Edinburgh, I focused on Audio Processing, Intelligent System and Autonomous Networks. Despite this, I spent the majority of my time in theatre and live events, working on technical production, set construction, event management and design. This inspired my MSc project, which was titled “Beat Tracking with Style Specific Heuristics”. Essentially, applications of real time beat tracking within live event production include synchronisation of lighting and sound, ideally with near-zero latency. This project was deemed a success when I identified that music genre classification could improve existing beat tracking algorithms, particularly when trying to identify beats within Jazz Music.