Our People
David Moffat
AI and Machine Learning Data Scientist
EO Instrumentation and Facilities
Research interests
I am an applied Artificial Intelligence and Machine Learning researcher who focuses on the application of techniques to Earth Observation data to formulate a better understanding of the natural world. I work across environmental domains to interpret and analyse data.
Recent publications
Ripple patterns on Martian barchan dunes in the north: indicators of the flow regime.. 2024-11-27
DOI: https://doi.org/10.5194/egusphere-egu24-667
Deciphering the variability in air-sea gas transfer due to sea state and wind history. 2024-09-02
DOI: https://doi.org/10.1093/pnasnexus/pgae389
Drone imagery and deep learning for mapping the density of wild Pacific oysters to manage their expansion into protected areas. 2024-07-08
DOI: http://dx.doi.org/10.1016/j.ecoinf.2024.102708
From digital assistant to digital collaborator. 2024-02-15
DOI: http://dx.doi.org/10.4324/9781003118817-20
TLS2trees: A scalable tree segmentation pipeline for TLS data. 2023-10-21
DOI: http://dx.doi.org/10.1111/2041-210x.14233
Determining drivers of phytoplankton carbon to chlorophyll ratio at Atlantic Basin scale. 2023-07-11
DOI: http://dx.doi.org/10.3389/fmars.2023.1191216
Analysis of Dune Ripple Patterns on the Surface of Earth and Mars to determine Local Sand Transport Conditions: A Machine Learning application.. 2023-05-15
DOI: https://doi.org/10.5194/egusphere-egu23-392
TLS2trees: a scalable tree segmentation pipeline for TLS data. 2022-12-11
DOI: http://dx.doi.org/10.1101/2022.12.07.518693
Word Embeddings for Automatic Equalization in Audio Mixing. 2022-11-02
DOI: http://dx.doi.org/10.17743/jaes.2022.0047
Semantic Music Production: A Meta-Study. 2022-07-25
DOI: http://dx.doi.org/10.17743/jaes.2022.0023
You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection. 2022-03
DOI: https://www.mdpi.com/2076-3417/12/7/3293
AI Music Mixing Systems. 2021-07-03
SOURCE-WORK-ID: http://dx.doi.org/10.1007/978-3-030-72116-9_13
Investigating the Effects of Training Set Synthesis for Audio Segmentation of Radio Broadcast. 2021-03-31
DOI: https://doi.org/10.3390/electronics10070827
A Deep Learning Approach to Intelligent Drum Mixing With the Wave-U-Net. 2021-03-09
DOI: https://www.aes.org/e-lib/browse.cfm?elib=21023
Context-Aware Intelligent Mixing Systems. 2021-03-09
DOI: https://www.aes.org/e-lib/browse.cfm?elib=21022
Mixing with Intelligent Mixing Systems: Evolving Practices and Lessons from Computer Assisted Design. 2020-05
EID: http://www.aes.org/e-lib/browse.cfm?elib=20793
A History of Audio Effects. 2020-01-22
DOI: https://doi.org/10.3390/app10030791
Approaches in Intelligent Music Production. 2019-09-25
DOI: http://dx.doi.org/10.3390/arts8040125
The Impact of Audio Effects Processing on the Perception of Brightness and Warmth. 2019-09-18
DOI: https://dl.acm.org/citation.cfm?id=3356618
An automated approach to the application of reverberation. 2019
EID: http://www.scopus.com/inward/record.url?eid=2-s2.0-85084096102&partnerID=MN8TOARS
Perceptual Evaluation of Synthesized Sound Effects. 2018-04
DOI: https://dl.acm.org/citation.cfm?id=3165287
Real-time physical model of an Aeolian harp. 2017
EID: http://www.scopus.com/inward/record.url?eid=2-s2.0-85029432788&partnerID=MN8TOARS
Unsupervised Taxonomy of Sound Effects. 2017
EID: https://dafx17.eca.ed.ac.uk/papers/DAFx17_Proceedings.pdf
