39 citations · 79 across the 8 of their papers we have counts for
8 papers
Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19
Davide Pigoli, Kieran Baker, Jobie Budd +19
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 voca…
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers
Harry Coppock, George Nicholson, Ivan Kiskin +22
Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, w…
A large-scale and PCR-referenced vocal audio dataset for COVID-19
Jobie Budd, Kieran Baker, Emma Karoune +23
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 sympt…
The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes
Björn W. Schuller, Anton Batliner, Shahin Amiriparian +12
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…
Super-resolution of Time-series Labels for Bootstrapped Event Detection
Ivan Kiskin, Udeepa Meepegama, Steven Roberts
Solving real-world problems, particularly with deep learning, relies on the availability of abundant, quality data. In this paper we develop a novel framework that maximises the ut…
Cost-sensitive detection with variational autoencoders for environmental acoustic sensing
Yunpeng Li, Ivan Kiskin, Davide Zilli +4
Environmental acoustic sensing involves the retrieval and processing of audio signals to better understand our surroundings. While large-scale acoustic data make manual analysis in…