ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Visual Transformers for Primates Classification and Covid Detection

Steffen Illium, Robert Müller, Andreas Sedlmeier, Claudia-Linnhoff Popien

We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings. When adding mel-based data augmentation techniques and sample-weighting, we achieve comparable performance on both (PRS and CCS challenge) tasks of ComParE21, outperforming most single model baselines. We further introduce overlapping vertical patching and evaluate the influence of parameter configurations.


doi: 10.21437/Interspeech.2021-273

Cite as: Illium, S., Müller, R., Sedlmeier, A., Popien, C.-L. (2021) Visual Transformers for Primates Classification and Covid Detection. Proc. Interspeech 2021, 451-455, doi: 10.21437/Interspeech.2021-273

@inproceedings{illium21_interspeech,
  author={Steffen Illium and Robert Müller and Andreas Sedlmeier and Claudia-Linnhoff Popien},
  title={{Visual Transformers for Primates Classification and Covid Detection}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={451--455},
  doi={10.21437/Interspeech.2021-273}
}