mlsanityData to 7 Oct 2026

Visual Transformers for Primates Classification and Covid Detection

arXiv 2212.10093 ↗First posted 20 Dec 2022cs.SD cs.CV cs.LGcited by 1

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Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 20 Dec 2022.

Builds on

16 of 23 references.

References · 16

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4 more
  1. 2021The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primatescited by 32
  2. 2021Introducing a Central African Primate Vocalisation Dataset for Automated Species Classificationcited by 4
  3. 2019Spec-ResNet: A General Audio Steganalysis scheme based on Deep Residual Network of Spectrogramcited by 3
  4. 2020Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrogramscited by 3

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Improving Primate Sounds Classification using Binary Presorting for Deep Learningcited by 0