在训练资源有限的音频分类中嵌入物理增强和小波散射变换的生成对抗网络

Kah Kuan Teh, T. H. Dat
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引用次数: 1

摘要

本文研究了训练资源有限的音频分类问题。我们首先研究了不同类型的数据增强,包括物理建模,小波散射变换和生成对抗网络(GAN)。然后,我们提出了一种新的GAN方法,将物理增强和小波散射变换嵌入到处理中。在Google Speech Command上的实验结果表明,在资源有限的情况下,本文提出的方法有明显的改进。当使用10%和25%的训练数据进行训练时,可以将分类准确率从ResNet上的最佳基线62.06%和77.29%提高到91.96%和93.38%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Embedding Physical Augmentation and Wavelet Scattering Transform to Generative Adversarial Networks for Audio Classification with Limited Training Resources
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.
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