Generative Adversarial Networks for Respiratory Sound Augmentation

Generative Adversarial Networks for Respiratory Sound Augmentation

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연구 분야: Artificial Intelligence

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학회: CCRIS '20: Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System

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초록

In this paper we propose to use generative adversarial network (GAN) for respiratory sound data augmentation. We present a GAN based approach that requires moderate amount of time and computing resources and capable to greatly increase performance of lung sound classification tasks. We also present a conditioned version of GAN, which is flexible and outperforms competitor augmentation methods. As a result, the GAN based augmentation method is able to boost RNN classifier performance by 10-15

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