--- layout: single title: "Mel-Vision Transformer" categories: research audio deep-learning anomalie-detection excerpt: "Attention based audio classification on Mel-Spektrograms" header: teaser: assets/figures/12_vision_transformer_teaser.jpg --- {:style="display:block; margin-left:auto; margin-right:auto"} 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. {% cite illium2021visual %} {:style="display:block; margin-left:auto; margin-right:auto"}