Visual Transformers for Primates Classification and Covid Detection
This work addresses audio classification for computational paralinguistics, but it is incremental as it applies an existing method to new data with minor modifications.
The authors tackled audio classification tasks for primate species and COVID-19 detection using vision transformers on mel-spectrograms, achieving comparable performance to baselines on ComParE21 challenges with techniques like data augmentation and sample-weighting.
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