Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Coppock, Harry"'
Despite significant advancements in deep learning for vision and natural language, unsupervised domain adaptation in audio remains relatively unexplored. We, in part, attribute this to the lack of an appropriate benchmark dataset. To address this gap
Externí odkaz:
http://arxiv.org/abs/2309.15024
Autor:
Budd, Jobie, Baker, Kieran, Karoune, Emma, Coppock, Harry, Patel, Selina, Cañadas, Ana Tendero, Titcomb, Alexander, Payne, Richard, Hurley, David, Egglestone, Sabrina, Butler, Lorraine, Mellor, Jonathon, Nicholson, George, Kiskin, Ivan, Koutra, Vasiliki, Jersakova, Radka, McKendry, Rachel A., Diggle, Peter, Richardson, Sylvia, Schuller, Björn W., Gilmour, Steven, Pigoli, Davide, Roberts, Stephen, Packham, Josef, Thornley, Tracey, Holmes, Chris
The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited volunta
Externí odkaz:
http://arxiv.org/abs/2212.07738
Autor:
Coppock, Harry, Nicholson, George, Kiskin, Ivan, Koutra, Vasiliki, Baker, Kieran, Budd, Jobie, Payne, Richard, Karoune, Emma, Hurley, David, Titcomb, Alexander, Egglestone, Sabrina, Cañadas, Ana Tendero, Butler, Lorraine, Jersakova, Radka, Mellor, Jonathon, Patel, Selina, Thornley, Tracey, Diggle, Peter, Richardson, Sylvia, Packham, Josef, Schuller, Björn W., Pigoli, Davide, Gilmour, Steven, Roberts, Stephen, Holmes, Chris
Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifie
Externí odkaz:
http://arxiv.org/abs/2212.08570
Autor:
Pigoli, Davide, Baker, Kieran, Budd, Jobie, Butler, Lorraine, Coppock, Harry, Egglestone, Sabrina, Gilmour, Steven G., Holmes, Chris, Hurley, David, Jersakova, Radka, Kiskin, Ivan, Koutra, Vasiliki, Mellor, Jonathon, Nicholson, George, Packham, Joe, Patel, Selina, Payne, Richard, Roberts, Stephen J., Schuller, Björn W., Tendero-Cañadas, Ana, Thornley, Tracey, Titcomb, Alexander
Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studi
Externí odkaz:
http://arxiv.org/abs/2212.08571
The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art within Computer Vision. As such, we present Audio Barlow Twins, a novel
Externí odkaz:
http://arxiv.org/abs/2209.14345
Autor:
Schuller, Björn W., Batliner, Anton, Amiriparian, Shahin, Bergler, Christian, Gerczuk, Maurice, Holz, Natalie, Larrouy-Maestri, Pauline, Bayerl, Sebastian P., Riedhammer, Korbinian, Mallol-Ragolta, Adria, Pateraki, Maria, Coppock, Harry, Kiskin, Ivan, Sinka, Marianne, Roberts, Stephen
The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on huma
Externí odkaz:
http://arxiv.org/abs/2205.06799
Autor:
Schuller, Björn W., Akman, Alican, Chang, Yi, Coppock, Harry, Gebhard, Alexander, Kathan, Alexander, Rituerto-González, Esther, Triantafyllopoulos, Andreas, Pokorny, Florian B.
Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the 13$^{th}$ SDG is a call for action to combat climate change for a better world. In this work, we provide
Externí odkaz:
http://arxiv.org/abs/2203.06064
Autor:
Coppock, Harry, Akman, Alican, Bergler, Christian, Gerczuk, Maurice, Brown, Chloë, Chauhan, Jagmohan, Grammenos, Andreas, Hasthanasombat, Apinan, Spathis, Dimitris, Xia, Tong, Cicuta, Pietro, Han, Jing, Amiriparian, Shahin, Baird, Alice, Stappen, Lukas, Ottl, Sandra, Tzirakis, Panagiotis, Batliner, Anton, Mascolo, Cecilia, Schuller, Björn W.
The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning fi
Externí odkaz:
http://arxiv.org/abs/2202.08981
Autor:
Akman, Alican, Coppock, Harry, Gaskell, Alexander, Tzirakis, Panagiotis, Jones, Lyn, Schuller, Björn W.
We report on cross-running the recent COVID-19 Identification ResNet (CIdeR) on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audio challenges: ComParE and DiCOVA. CIdeR is an end-to-end deep learning neural network originally des
Externí odkaz:
http://arxiv.org/abs/2107.14549
Autor:
Coppock, Harry, Gaskell, Alexander, Tzirakis, Panagiotis, Baird, Alice, Jones, Lyn, Schuller, Björn W.
Our main contributions are as follows: (I) We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples, achieving ROC-AUC of 0.846; (II) Our model, the COVID-19 Identification Res
Externí odkaz:
http://arxiv.org/abs/2102.08359