Search Results for author: Harry Coppock

Found 7 papers, 2 papers with code

Audio Barlow Twins: Self-Supervised Audio Representation Learning

1 code implementation28 Sep 2022 Jonah Anton, Harry Coppock, Pancham Shukla, Bjorn W. Schuller

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.

Environmental Sound Classification Event Detection +2

The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes

no code implementations13 May 2022 Björn W. Schuller, Anton Batliner, Shahin Amiriparian, Christian Bergler, Maurice Gerczuk, Natalie Holz, Pauline Larrouy-Maestri, Sebastian P. Bayerl, Korbinian Riedhammer, Adria Mallol-Ragolta, Maria Pateraki, Harry Coppock, Ivan Kiskin, Marianne Sinka, Stephen Roberts

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 human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected.

Human Activity Recognition

Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet

no code implementations10 Mar 2022 Björn W. Schuller, Alican Akman, Yi Chang, Harry Coppock, Alexander Gebhard, Alexander Kathan, Esther Rituerto-González, Andreas Triantafyllopoulos, Florian B. Pokorny

We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether, proposed by the ancient Greeks in their five element theory; this categorisation serves as a framework to discuss computer audition in relation to different ecological aspects.

Time Series Forecasting

Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges

no code implementations30 Jul 2021 Alican Akman, Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis, Lyn Jones, Björn W. Schuller

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.

COVID-19 Diagnosis

End-2-End COVID-19 Detection from Breath & Cough Audio

1 code implementation7 Jan 2021 Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis, Alice Baird, Lyn Jones, Björn W. Schuller

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 ResNet, (CIdeR), has potential for rapid scalability, minimal cost and improving performance as more data becomes available.

Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks

no code implementations29 Dec 2020 Björn W. Schuller, Harry Coppock, Alexander Gaskell

The COVID-19 pandemic has affected the world unevenly; while industrial economies have been able to produce the tests necessary to track the spread of the virus and mostly avoided complete lockdowns, developing countries have faced issues with testing capacity.

Bayesian Optimisation

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