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EnViTSA: Ensemble of Vision Transformer with SpecAugment for Acoustic Event Classification.

Kian Ming Lim1, Chin Poo Lee1, Zhi Yang Lee2

  • 1Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia.

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|November 25, 2023
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Summary

EnViTSA, using Vision Transformers and SpecAugment, improves Acoustic Event Classification by reducing overfitting and enhancing accuracy on benchmark datasets.

Keywords:
acoustic event classificationensemble learninglog mel-spectrogramsspecaugmentvision transformer

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Signal Processing

Background:

  • Deep learning models for Acoustic Event Classification (AEC) face overfitting challenges due to high complexity.
  • Existing methods struggle with data scarcity and generalization in acoustic event detection.

Purpose of the Study:

  • To introduce EnViTSA, an innovative approach for Acoustic Event Classification.
  • To enhance AEC performance by mitigating overfitting and addressing data scarcity.

Main Methods:

  • Ensemble of pre-trained Vision Transformers combined with SpecAugment data augmentation.
  • Transformation of raw acoustic signals into Log Mel-spectrograms.
  • Application of time and frequency masking via SpecAugment to generate synthetic training data.

Main Results:

  • Achieved 93.50% accuracy on ESC-10, 85.85% on ESC-50, and 83.20% on UrbanSound8K.
  • Demonstrated significant reduction in overfitting through the ensemble approach.
  • Validated the effectiveness of SpecAugment for AEC data augmentation.

Conclusions:

  • EnViTSA offers a substantial advancement in Acoustic Event Classification.
  • Vision Transformers and SpecAugment show strong potential for acoustic domain applications.
  • The proposed method effectively tackles key challenges in deep learning for AEC.