• Corpus ID: 238744062

EIHW-MTG DiCOVA 2021 Challenge System Report

@article{MallolRagolta2021EIHWMTGD2,
  title={EIHW-MTG DiCOVA 2021 Challenge System Report},
  author={Adria Mallol-Ragolta and Helena Cuesta and Emilia G'omez and Bj{\"o}rn Schuller},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.06543}
}
This paper aims to automatically detect COVID-19 patients by analysing the acoustic information embedded in coughs. COVID-19 affects the respiratory system, and, consequently, respiratory-related signals have the potential to contain salient information for the task at hand. We focus on analysing the spectrogram representations of coughing samples with the aim to investigate whether COVID-19 alters the frequency content of these signals. Furthermore, this work also assesses the impact of gender… 

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