Embedding Physical Augmentation and Wavelet Scattering Transform to Generative Adversarial Networks for Audio Classification with Limited Training Resources

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Embedding Physical Augmentation and Wavelet Scattering Transform to Generative Adversarial Networks for Audio Classification with Limited Training Resources
Title:
Embedding Physical Augmentation and Wavelet Scattering Transform to Generative Adversarial Networks for Audio Classification with Limited Training Resources
Journal Title:
2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publication Date:
17 April 2019
Citation:
K.K.Teh, Tran Huy Dat, "Embedding Physical Augmentation and Wavelet Scattering Transform to Generative Adversarial Networks for Audio Classification with Limited Training Resources," In ICASSP, 2019
Abstract:
This paper addresses audio classification with limited training resources. We first investigate different types of data augmentation including physical modeling, wavelet scattering transform and Generative Adversarial Networks (GAN). We than propose a novel GAN method to embed physical augmentation and wavelet scattering transform in processing. The experimental results on Google Speech Command show significant improvements of the proposed method when training with limited resources. It could lift up classification accuracy from the best baselines of 62.06% and 77.29% on ResNet, to as far as 91.96% and 93.38%, when training with 10% and 25% training data, respectively.
License type:
PublisherCopyrights
Funding Info:
Description:
(C) 2019 IEEE
ISSN:
1520-6149
1520-6149
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