Dr Dave Moffat
Research Assistant in Audio Signal Processing and AI
Faculty of Arts, Humanities and Business
David Moffat is a lecturer in Sound and Music Computing. He is also a collaborator on the RadioMe project. His research focuses is on intelligent and assistive mixing and audio production tools through the implementation of semantic tools and machine learning.He previously worked as a postdoc within the Audio Engineering Group of the Centre for Digital Music at Queen Mary University London. His PhD, from Queen Mary University London, focused on sound effect synthesis and evaluation. He has since developed new methods and approaches for sound synthesis, from this understanding of the field. He received an MSc in Digital Music Processing from Queen Mary University of London, and a BSc in Artificial Intelligence and Computer Science from University of Edinburgh.
PhD in Computer Science and Audio, Queen Mary University of London.
Diploma in Researcher Development, Queen Mary University of London.
MSc (Dist.) in Digital Signal Processing, Queen Mary University of London.
BSc(Hons.) in Artificial Intelligence and Computer Science, University of Edinburgh.
Roles on external bodies
Digital Audio Effects Conference Paper Chair - September 2019
ACM Audio Mostly Conference Committee Member - August 2017
Chair, Audio Engineering Society (AES) Student Delegate Assembly (Europe and International) - June 2016 - May 2017
Vice Chair, AES Student Delegate Assembly - May 2015 - June 2016
Conference Committee, AES 61st Conference, Audio for Games - February 2016
AES Education Committee Member - May 2015 - Present
Member of the IEEE
Member of The Audio Engineering Society (AES)
Reviewer for AHRC (Arts and Humanities Research Council)
Reviewer for MDPI Electronics
Reviewer for Leonardo Journal
Reviewer for IEEE Transactions on Audio, Speech and Language
Reviewer for Web Audio Conference
Reviewer for International Conference on Digital Audio Effects
Reviewer for Audio Engineering Society Convention
Reviewer for Audio Mostly Conference
Reviewer for Computer Music Multidisciplinary Research Conference
My research interests include audio production technology, artificial intelligence, real time and live mixing tools and DSP, machine learning and deep learning, sound synthesis, procedural audio, music information retrieval (MIR), source separation, audio engineering technology, genetic algorithms, audio effects and real time analysis and manipulation of audio. Perceptive, qualitative and objective measures and metrics for the evaluation of audio technologies.
Key publications are highlightedJournals