  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. Index Terms: audio classification, attention,
mel-spectrogram, unbalanced data-sets, computational paralinguistics
