Harry Coppock, et al., Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers, Nature Machine Intelligence, 2024 [BIB] [PDF]
Chia-Hsin Lin, Charles Jones, Björn W. Schuller, Harry Coppock, Synthia's Melody: A Benchmark Framework for Unsupervised Domain Adaptation in Audio, NeurIPS 2023 Workshop Machine Learning for Audio, 2024 [BIB] [PDF]
Jonah Anton, Harry Coppock, Pancham Shukla, Bjorn W.Schuller, Audio Barlow Twins: Self-Supervised Audio Representation Learning, ICASSP, 2023 [BIB] [PDF]
Harry Coppock, Lyn Jones, Ivan Kiskin, Björn W. Schuller, Covid-19 detection from audio: Seven grains of salt, The Lancet Digital Health doi:10.1016/S2589-7500(21)00141-2 [BIB] [PDF]
Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis, Alice Baird, Lyn Jones, Björn W Schuller (2021) End-2-End convolutional neural network enables COVID-19 Detection from Breath & Cough Audio: a pilot study BMJ innovations [BIB] [PDF]
Mina A. Nessiem, Mostafa M. Mohamed, Harry Coppock, Alexander Gaskell, Björn W. Schuller (2020) Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks IEEE CBMS' [ Best Student Paper Award at The 34th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2021) ][BIB] [PDF]
Harry Coppock, Björn W Schuller Vector Quantised-Variational Autoencoders(VQ-VAEs) for Representation learning (2020) Master Thesis [ Imperial College London Distinguised Project Award ][PDF]