CV

Education

Associate Fellow Higher Education Academy (AFHEA), July 2021
Ph.D in Computer Science, Queen Mary University of London, October 2019
Diploma in Reseracher Revelopment, Queen Mary University of London, October 2019
M.Sc. in Digital Music Processing, Queen Mary University of London, November 2014
B.Sc. (Hons) in Artifical Intelligence and Computer Science, University of Edinburgh, June 2011

Work experience

AI and Data Science Lead
- Plymouth Marine Laboratory, Sept. 2021 – Present
Lecturer in Sound and Music Computing
- University of Plymouth, Sept. 2020 – Sept. 2021
- Duties included: Engage with teaching and administrative duties on the Computing, Audio and Music Technology BSc. Programme. Teaching courses on topics including computer programming, audio engineering technology and research methods. As the programme leader for the course, I perform administrative tasks, attend open days, and engage in student recruitment events. Presenting research, including outreach and engagement work and grant writing.
Postdoctoral Researcher in AI and Signal Processing
- University of Plymouth, Nov. 2019 – Sept. 2021
- Duties included: Performing research on adaptive radio technology systems for people with dementia, as part of the RadioMe project. Applications of artificial intelligence and machine learning to audio mixing, source separation, speech synthesis, signal analysis and real-time audio processing. Developing engineering solutions and research around medial application of audio technology
Postdoctoral Researcher
- Queen Mary University of London, May 2018 -– Oct. 2019
- Duties included: Published research on intelligent audio production, as part of FAST-IMPACt project. Applications of artificial intelligence and machine learning to audio mixing technologies, with a focus on semantic enhancements to studio production through audio analysis and expert knowledge.
Freelance Audio DSP Consultant and Developer
- Self Employed, Sept. 2013 –- Present
- Working on projects as freelance audio technology consultant, working on numerous freelance projects including:
  - Venture Capitalist technology review and due diligence consultant
  - Technology business advice and consultancy for startup business.
  - MIR and audio analysis research and implementation, including embed technology in backend web
  - services for cloud solutions
  - Development and implementation of new set of audio effects for real time embedded Arm Cortex M4
  - hardware system.
  - Development of VST plugins using JUCE framework
  - Develop and implement real time processing system of Beagle Bone Black, using Bela realtime platform
  - Musical sound synthesis developer and machine learning consultant for musical interactive project

Skills

Experienced in research and engineering principals and methods, academic writing, experimental design, statistics, digital signal processing, and machine learning.
Teaching experience including lecturing course material up to postgraduate level, guest lecturing, supporting postgraduate and undergraduate project students, developing new modules, designing and marking coursework, and recognised Associate Fellow of the HEA (AFHEA).
Knowledgeable in machine learning, audio signal processing and their applications in audio effects, intelligent audio production, acoustics and source separation.
Active within academic networks, regularly publishing within communities, performing peer review and organising academic conferences.
Strong programming background and experience, including Python, C, C++, Matlab, Pure Data, BASH scripting, LaTeX, and version control systems including Git and Mercurial.
Experienced Project Manager and Supervisory roles. Technical expertise with audio event production and recording.

Publications

Journal Publications

Beyond chlorophyll: machine learning estimates of diagnostic phytoplankton pigments from multispectral ocean colour data Permalink

David Moffat, Angus Laurenson, Victor Martinez-Vicente, Gemma Kulk, Xuerong Sun, Robert J. W. Brewin and Shubha Sathyendranath "Beyond chlorophyll: machine learning estimates of diagnostic phytoplankton pigments from multispectral ocean colour data", arXiv:2608.23348 [q-bio.OT], 2026.

Deep learning model emulators for marine biogeochemistry forecasting from days to decades Permalink

Jozef Skakala, Ieuan Higgs, David Moffat "Deep learning model emulators for marine biogeochemistry forecasting from days to decades", arXiv:2606.27168, 2026.

Mixture Density Networks Improve Inter-Sensor Consistency of Optical Water Type Classification Permalink

D. Lomeo, S. Simis, M. Warren, D. Moffat, A. Jungblut and E. Tebbs "Mixture Density Networks Improve Inter-Sensor Consistency of Optical Water Type Classification", Transactions on Geoscience and Remote Sensing, 2026.

Modelling primary production: multitude of theories, or multitude of languages? Permalink

Skákala, J., Sathyendranath, S., Artioli, Y., Banerjee, D. S., Bouman, H., Brewin, R. J. W., Butenschön, M., Ciavatta, S., Dutkiewicz, S., Fidai, Y., Ford, D., George, G., Guihou, K., Jönsson, B., Bačeković Koloper, M., Kovač, Ž., Krishnakumary, L., Kulk, G., Laufkötter, C., Lessin, G., Mattern, J. P., Melet, A., Mignot, A., Moffat, D., Monteiro, F., Rodriguez Bennadji, M., Rousseaux, C., Swaminathan, R., Ulloa, O., and Tjiputra, J. "Modelling primary production: multitude of theories, or multitude of languages? ", Ocean Science, 3.22; EGUsphere, 2026. 1-39

ForestScan: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data Permalink

Chavana-Bryant, C., Wilkes, P., Yang, W., Burt, A., Vines, P., Bennett, A. C., Pickavance, G. C., Cooper, D. L. M., Lewis, S. L., Phillips, O. L., Brede, B., Lau, A., Herold, M., McNicol, I. M., Mitchard, E. T. A., Coomes, D. A., Jackson, T. D., Makaga, L., Milamizokou Napo, H. O., Ngomanda, A., Ntie, S., Medjibe, V., Dimbonda, P., Soenens, L., Daelemans, V., Proux, L., Nilus, R., Labrière, N., Jeffery, K., Burslem, D. F. R. P., Clewley, D., Moffat, D., Qie, L., Bartholomeus, H., Vincent, G., Barbier, N., Derroire, G., Abernethy, K., Scipal, K., and Disney, M. "ForestScan: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data." Earth System Science Data, 18, 1243–1274 February 2026.

Consistency of clustering analysis of complex 3D ocean datasets

Rebecca Millington, Dale Partridge, Helen R. Powley, Gennadi Lessin, David Moffat and Jerry Blackford, "Consistency of clustering analysis of complex 3D ocean datasets." Ecological Informatics

SII-NowNet: A deep learning framework for nowcasting convection initiation and intensification in the Tropics

Joseph Smith, Cathryn Birch, John Marsham, and David Moffat, "SII-NowNet: A deep learning framework for nowcasting convection initiation and intensification in the Tropics." Artificial Intelligence for the Earth Systems (AIES)

A Deep Learning Approach to Downscaling Microwave Land Surface Temperatures for a Clear-Sky Merged Infrared-Microwave Product

Abigail Marie Waring, Darren Ghent, David Moffat, Carlos Jimenez and John Remedios, "A Deep Learning Approach to Downscaling Microwave Land Surface Temperatures for a Clear-Sky Merged Infrared-Microwave Product." Remote Sensing 2025, 17(23), 3893

A Sentinel-3 foundation model for ocean colour

Dawson, G., Vandaele, R., Taylor, A., Moffat, D., Tamura-Wicks, H., Jackson, S., Lickorish, R., Fraccaro, P., Williams, H., Luo, C. and Jones, A., 2025. "A Sentinel-3 foundation model for ocean colour". arXiv preprint arXiv:2509.21273

Segmenting and characterising ripple patterns on sand dunes using machine learning and 2D semi-variogram

Lucie A. Delobel, David Moffat, Emma Tebbs, Andreas C.W. Baas, "Segmenting and characterising ripple patterns on sand dunes using machine learning and 2D semi-variogram." Remote Sensing of Environment Volume 331 (2025): 115031

Sensing human health from space: An assessment of applications and big data platforms

D. Sengupta, F. G. Bandao, S. Sathyendranath, G. Kulk, A. Conte, C. Ippolitti, L. Candeloro, D. Moffat, W. Wint, M. Maranesi, R. Scarano, J. Vitorino, G. Brandt and T. M Harish "Sensing human health from space: An assessment of applications and big data platforms." Remote Sensing Applications: Society and Environment 2025

The theoretical role of the wind in aerosolising microplastics and nanoplastics from coastal combined sewer overflows Permalink

Lauren Biermann, David Moffat, Clive E. Sabel and Thomas E. Stovin "The theoretical role of the wind in aerosolising microplastics and nanoplastics from coastal combined sewer overflows." Nature Scientific Reports 15, 23623. July 2025

Deciphering the variability in air-sea gas transfer due to sea state and wind history Permalink

Ming-Xi Yang, David Moffat, Yuanxu Dong and Jean-Raymond Bidlot "Deciphering the variability in air-sea gas transfer due to sea state and wind history." PNAS Nexus, pgae389. September 2024

Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas Permalink

Aser Mata, David Moffat, Sílvia Almeida, Marko Radeta, William Jay, Nigel Mortimer, Katie Awty-Carroll, Oliver R. Thomas, Vanda Brotas, Steve Groom "Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas." Ecological Informatics. 102708, July 2024

Determining drivers of phytoplankton carbon to chlorophyll ratio at Atlantic Basin scale Permalink

Timothy J. Smyth, David Moffat, Glen A. Tarran, Shubha Sathyendranath, François Ribalet and John Casey "Determining drivers of phytoplankton carbon to chlorophyll ratio at Atlantic Basin scale." Frontiers in Marine Science. Volume 10, July 2023

TLS2trees: a scalable tree segmentation pipeline for TLS data Permalink

Phil Wilkes, Mathias Disney, John Armston, Harm Bartholomeus, Lisa Bentley, Benjamin Brede, Andrew Burt, Kim Calders, Cecilia Chavana-Bryant, Daniel Clewley, Laura Duncanson, Brieanne Forbes, Sean Krisanski, Yadvinder Malhi, David Moffat, Niall Origo, Alexander Shenkin, Wanxin Yang, "TLS2trees: a scalable tree segmentation pipeline for TLS data." Methods in Ecology and Evolution. Wiley. October 2023.

Word Embeddings for Automatic Equalization in Audio Mixing Permalink

Satvik Venkatesh, David Moffat, Eduardo Miranda, "Word Embeddings for Automatic Equalization in Audio Mixing." Journal of the Audio Engineering Society, Vol 70 no.9 pp. 753-763; September 2022

Semantic Music Production: A Meta-study

David Moffat, Brecht de Man and Joshua D. Reiss, "Semantic Music Production: A Meta-study." Journal of the Audio Engineering Society, vol. 70, no. 7/8, pp. 548-564, July 2022

You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection Permalink

Satvik Venkatesh, David Moffat, Eduardo Miranda, "You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection." Applied Sciences, 12, 3293. 2022. DOI 10.3390/app12073293

Investigating the Effects of Training Set Synthesis for Audio Segmentation of Radio Broadcast

Satvik Venkatesh, David Moffat, Eduardo Miranda, "Investigating the Effects of Training Set Synthesis for Audio Segmentation of Radio Broadcast." Electronics, 2021.

Context Aware Intelligent Mixing Systems

M. Lefford, Gary Bromham, György Fazekas, David Moffat, "Context Aware Intelligent Mixing Systems." Journal of the Audio Engineering Society, 2021.

A Deep Learning Approach to Intelligent Drum Mixing with the Wave-U-Net

Marco Martínez, Daniel Stoller, David Moffat, "A Deep Learning Approach to Intelligent Drum Mixing with the Wave-U-Net." Journal of the Audio Engineering Society, 2021.

A History of Audio Effects

Thomas Wilmering, David Moffat, Alessia Milo, Mark Sandler, "A History of Audio Effects." Applied Sciences, 2020.

Approaches in Intelligent Music Production

David Moffat, Mark Sandler, "Approaches in Intelligent Music Production." Arts, 2019.

Creating Real-Time Aeroacoustic Sound Effects Using Physically Derived Models

Rod Selfridge, David Moffat, Eldad Avital, Joshua Reiss, "Creating Real-Time Aeroacoustic Sound Effects Using Physically Derived Models." Journal of the Audio Engineering Society, 2018.

Perceptual Evaluation of Synthesized Sound Effects

David Moffat, Joshua Reiss, "Perceptual Evaluation of Synthesized Sound Effects." ACM Transactions on Applied Perception (TAP), 2018.

Sound Synthesis of Objects Swinging Through Air Using Physical Models

Rod Selfridge, David Moffat, Joshua Reiss, "Sound Synthesis of Objects Swinging Through Air Using Physical Models." Applied Sciences, 2017.

What do your footsteps sound like? An investigation on interactive footstep sounds adjustment

Luca Turchet, David Moffat, Ana Tajadura-Jiménez, Joshua Reiss, Tony Stockman, "What do your footsteps sound like? An investigation on interactive footstep sounds adjustment." Applied Acoustics, 2016.

Book Chapters

From Intelligent Digital Assistant to Intelligent Digital Collaborator

M. Nyssim Lefford, David Moffat and Gary Bromham, "From Intelligent Digital Assistant to Intelligent Digital Collaborator." In Innovation in Music Performance: Technology and Creativity. (pp. 278-291) Editors Jan-Olof Gullö, Russ Hepworth-Sawyer, Justin Paterson, Rob Toulson and Mark Marrington. Focal Press. March 2024

A History of Distortion

Clive Mead, David Moffat and Gary Bromham, "A History of Distortion." In Distortion in Music Production, The Soul of Sonics. Chapter 1, Editors Gary Bromham and Austin Moore. Focal Press, June 2023.

AI Music Mixing Systems

David Moffat, "AI Music Mixing Systems." In the proceedings of Handbook of Artificial Intelligence for Music, Chapter 13, 2021.

Sound Effect Synthesis

David Moffat, Rod Selfridge, Joshua Reiss, "Sound Effect Synthesis." In the proceedings of Foundations in Sound Design for Interactive Media: A Multidisciplinary Approach, Chapter 13, 2019.

Conference Publications

ViT-based Broad Learning System for Remote Sensing Scene Classification

Z. Liu, C. Luo, Z. Fu, and D. Moffat "ViT-based Broad Learning System for Remote Sensing Scene Classification" In the proceedings of IEEE 2026 International Geoscience and Remote Sensing Symposium, 2026.

RadioMe: Supporting Individuals with Dementia in Their Own Home… and Beyond

Di Campli San Vito, P., Brewster, S., Venkatesh, S., Miranda, E., Kirke, A., Moffat, D., Banerjee, S., Street, A., Fachner, J. and Odell-Miller "RadioMe: Supporting Individuals with Dementia in Their Own Home... and Beyond?" In the proceedings of 2022 CHI Conference on Human Factors in Computing Systems (CHI 22) Workshop 32, New Orleans, LA, USA, 30 Apr 2022.

Artificially synthesising data for audio classification and segmentation to improve speech and music detection in radio broadcast

Satvik Venkatesh, David Moffat, Alexis Kirke, Gözel Shakeri, Stephen Brewster, Jörg Fachner, Helen Odell-Miller, Alex Street, Nicolas Farina, Sube Banerjee, Eduardo Miranda, "Artificially synthesising data for audio classification and segmentation to improve speech and music detection in radio broadcast." In the proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.

RadioMe: Challenges During the Development of a Real Time Tool to Support People With Dementia

Shakeri Gozel, Stephen Brewster, Satvik Venkatesh, David Moffat, Alexis Kirke, Eduardo Miranda, Sube Banerjee, Alex Street, Jörg Fachner, Helen Odell-Miller, "RadioMe: Challenges During the Development of a Real Time Tool to Support People With Dementia." In the proceedings of Conference on Human Factors in Computing Systems (CHI), 2021.

Composing, Recording and Producing with Historical Equipment and Instrument Models

Clive Mead, David Moffat, Eduardo Miranda, "Composing, Recording and Producing with Historical Equipment and Instrument Models." In the proceedings of Audio Engineering Society Convention 139, 2020.

Mixing with Intelligent Mixing Systems: Evolving Practices and Lessons from Computer Assisted Design

M. Lefford, Gary Bromham, David Moffat, "Mixing with Intelligent Mixing Systems: Evolving Practices and Lessons from Computer Assisted Design." In the proceedings of Audio Engineering Society Convention 148, 2020.

Retro in Digital: Understanding the Semantics of Audio Effects

Gary Bromham, David Moffat, Mathieu Barthet, György Fazekas, "Retro in Digital: Understanding the Semantics of Audio Effects." In the proceedings of DMRN+14: Digital Music Reserach Network One-Day Workshop, 2019.

RadioMe: Artificially Intelligent Radio for People with Dementia

Satvik Venkatesh, David Moffat, Eduardo Miranda, "RadioMe: Artificially Intelligent Radio for People with Dementia." In the proceedings of DMRN+14: Digital Music Research Network One-Day Workshop, 2019.

Machine Learning Multitrack Gain Mixing of Drums

David Moffat, Mark Sandler, "Machine Learning Multitrack Gain Mixing of Drums." In the proceedings of Audio Engineering Society Convention 147, 2019.

An Automated Approach to the Application of Reverberation

David Moffat, Mark Sandler, "An Automated Approach to the Application of Reverberation." In the proceedings of Audio Engineering Society Convention 147, 2019.

The Impact of Audio Effects Processing on the Perception of Brightness and Warmth

Gary Bromham, David Moffat, Mathieu Barthet, Anne Danielsen, György Fazekas, "The Impact of Audio Effects Processing on the Perception of Brightness and Warmth." In the proceedings of ACM Audio Mostly Conference, 2019.

Automatic Mixing Level Balancing Enhanced through Source Interference Identification

David Moffat, Mark Sandler, "Automatic Mixing Level Balancing Enhanced through Source Interference Identification." In the proceedings of Audio Engineering Society Convention 146, 2019.

The impact of compressor ballistics on the perceived style of music

Gary Bromham, David Moffat, György Fazekas, Mathieu Barthet, Mark Sandler, "The impact of compressor ballistics on the perceived style of music." In the proceedings of Audio Engineering Society Convention 145, 2018.

Adaptive Ballistics Control of Dynamic Range Compression for Percussive Tracks

David Moffat, Mark Sandler, "Adaptive Ballistics Control of Dynamic Range Compression for Percussive Tracks." In the proceedings of Audio Engineering Society Convention 145, 2018.

Towards a Semantic Web Representation and Application of Audio Mixing Rules

David Moffat, Florian Thalmann, Mark Sandler, "Towards a Semantic Web Representation and Application of Audio Mixing Rules." In the proceedings of Proceedings of the 4th Workshop on Intelligent Music Production (WIMP), 2018.

Objective Evaluations of Synthesised Environmental Sounds

David Moffat, Joshusa Reiss, "Objective Evaluations of Synthesised Environmental Sounds." In the proceedings of Proceedings of the 21th International Conference on Digital Audio Effects (DAFx-18), 2018.

Unsupervised Taxonomy of Sound Effects

David Moffat, David Ronan, Joshusa Reiss, "Unsupervised Taxonomy of Sound Effects." In the proceedings of Proc. 20th International Conference on Digital Audio Effects (DAFx-17), 2017.

Physically Derived Sound Synthesis Model of a Propeller

Rod Selfridge, David Moffat, Joshua Reiss, "Physically Derived Sound Synthesis Model of a Propeller." In the proceedings of ACM Audio Mostly Conference, 2017.

Real-Time Physical Model for an Aeolian Harp

Rod Selfridge, David Moffat, Joshua Reiss, Eldad Avital, "Real-Time Physical Model for an Aeolian Harp." In the proceedings of Proceedings of the 24th International Congress on Sound and Vibration, 2017.

Real-Time Physical Model for Synthesis of Sword Swing Sounds

Rod Selfridge, David Moffat, Joshua Reiss, "Real-Time Physical Model for Synthesis of Sword Swing Sounds." In the proceedings of Proceedings of the 14th International Conference on Sound and Music Computing (SMC), 2017.

Perceptual evaluation of synthesised sound effects

David Moffat, Joshua Reiss, "Perceptual evaluation of synthesised sound effects." In the proceedings of DMRN+11: Digital Music Research Network Workshop, 2016.

Subjective comparison of music production practices using the Web Audio Evaluation Tool

Brecht De, Nicholas Jillings, David Moffat, Joshua Reiss, Ryan Stables, "Subjective comparison of music production practices using the Web Audio Evaluation Tool." In the proceedings of 2nd AES Workshop on Intelligent Music Production, 2016.

Web Audio Evaluation Tool: A framework for subjective assessment of audio

Nicholas Jillings, Brecht De, David Moffat, Joshua Reiss, "Web Audio Evaluation Tool: A framework for subjective assessment of audio." In the proceedings of Proceedings of the 2nd Web Audio Conference, 2016.

Modal Synthesis of Weapon Sounds

Lucas Mengual, David Moffat, Joshua Reiss, "Modal Synthesis of Weapon Sounds." In the proceedings of Proceedings of the Audio Engineering Society Conference: 61st International Conference: Audio for Games, 2016.

Implementation and Assessment of Joint Source Separation and Dereverberation

David Moffat, Joshua Reiss, "Implementation and Assessment of Joint Source Separation and Dereverberation." In the proceedings of Proceedings of the Audio Engineering Society Conference: 60th International Conference: DREAMS (Dereverberation and Reverberation of Audio, Music, and Speech), 2016.

Automatic Subgrouping of Multitrack Audio

David Ronan, David Moffat, Hatice Gunes, Joshua Reiss, "Automatic Subgrouping of Multitrack Audio." In the proceedings of Proceedings of the 18th International Conference on Digital Audio Effects (DAFx-15), 2015.

An Evaluation of Audio Feature Extraction Toolboxes

David Moffat, David Ronan, Joshusa Reiss, "An Evaluation of Audio Feature Extraction Toolboxes." In the proceedings of Proceedings of the 18th International Conference on Digital Audio Effects (DAFx-15), 2015.

Web Audio Evaluation Tool: A Browser-Based Listening Test Environment

Nicholas Jillings, Brecht De, David Moffat, Joshua Reiss, "Web Audio Evaluation Tool: A Browser-Based Listening Test Environment." In the proceedings of Proceedings of the Sound and Music Computing Conference, 2015.

Talks

Supporting Environmental Research with Artificial Intelligence at the NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS)

July 06, 2022

Conference proceedings talk at 2022 UK Conference on Environmental Data Science, Lancaster, UK

Deep-learning detection of Harmful Algal Blooms

September 08, 2022

Conference proceedings talk at UK National Earth Observation Conference 2022,, Leicester, UK

Challenges and Solutions for using Machine Learning with Earth Observation Data

September 08, 2022

Conference proceedings talk at UK National Earth Observation Conference 2022,, Leicester, UK

Deep-learning detection of harmful algal blooms

October 12, 2022

Talk at University of Exeter, Institute for Data Science and Artificial Intelligence, Exeter, UK

Computer Science: Connecting Music and Environment Science

March 16, 2023

Guest Lecture at Outside The Box Course, Computer Science Dept., University of Exeter, Exeter, UK

An Introduction to Machine Learning for Earth Observation

March 27, 2023

Guest Lecture at Open Network for Water-Related Diseases, Online Webinar

Plymouth Marine Laboratory, Recent developments in Machine Learning and Remote Sensing

April 18, 2023

Working Group Meeting Invited Presentation at The 55th Meeting of the Working Group on Information Systems and Services, Committee on Earth Observation Satellites, Córdoba, Argentina (Remote Talk)

Panel Discussion: Can ML4EO save the world?

April 20, 2023

Panel Member at Machine Learning for Earth Observation Remote Sensing with the Environment Intelligence Network, Exeter, UK

Machine Learning and Remote Sensing: An Overview of ongoing work