Corpus ID: 227254207

A Study of Few-Shot Audio Classification

@article{Wolters2020ASO,
  title={A Study of Few-Shot Audio Classification},
  author={Piper Wolters and Chris Careaga and Brian Hutchinson and Lauren Phillips},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.01573}
}
Advances in deep learning have resulted in state-of-the-art performance for many audio classification tasks but, unlike humans, these systems traditionally require large amounts of data to make accurate predictions. Not every person or organization has access to those resources, and the organizations that do, like our field at large, do not reflect the demographics of our country. Enabling people to use machine learning without significant resource hurdles is important, because machine learning… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 23 REFERENCES
Few Shot Speaker Recognition using Deep Neural Networks
  • 14
  • PDF
Metric-Based Few-Shot Learning for Video Action Recognition
  • 5
  • PDF
Siamese Neural Networks for One-Shot Image Recognition
  • 1,703
  • PDF
Training Neural Audio Classifiers with Few Data
  • Jordi Pons, J. Serrà, X. Serra
  • Computer Science, Engineering
  • ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2019
  • 20
  • PDF
Matching Networks for One Shot Learning
  • 2,373
  • PDF
Learning to Compare: Relation Network for Few-Shot Learning
  • 1,104
  • PDF
Deep Neural Network Approaches to Speaker and Language Recognition
  • 320
  • PDF
Prototypical Networks for Few-shot Learning
  • 2,044
  • PDF
Deep neural network features and semi-supervised training for low resource speech recognition
  • 113
  • PDF
Transfer Learning for Speech Recognition on a Budget
  • 57
  • PDF
...
1
2
3
...