CP-GAN: Context Pyramid Generative Adversarial Network for Speech Enhancement
Gang Liu, Ke Gong, Xiaodan Liang, Zhiguang Chen
Abstract
The topic of speech enhancement has been largely improved recently, especially with the development of generative adversarial networks (GANs). However prior methods simply follow the GAN architectures from computer vision tasks without specific designs for the speech enhancement according to the audio characteristics (i.e., different granularity context), which may leave noise points in some segments or disturb the contents of the original audio. In this work, we make the first attempt to explore the global and local speech features for coarse-to-fine speech enhancement and introduce a Context Pyramid Generative Adversarial Network (CPGAN), which contains a densely-connected feature pyramid generator and a dynamic context granularity discriminator to better eliminate audio noise hierarchically. Extensive experiments demonstrate that our CP-GAN effectively achieves state-of-the-art speech enhancement results and boosts the performance of more high-level speech tasks including automatic speech recognition and speaker recognition.