End-to-End Speech Enhancement Using Fully Convolutional Networks with Skip Connections

Abstract

The purpose of speech enhancement is to extract useful speech signal from noisy speech. The performance of speech enhancement has been improved greatly in recent years with fast development of the deep learning. However, these studies mainly focus on the frequency domain, which needs to complete time-frequency transformation and the phase information of speech is ignored. Therefore, the end-to-end (i.e. waveform-in and waveform-out) speech enhancement was investigated, which not only avoids fixed time-frequency transformation but also allows modelling phase information. In this paper, a fully convolutional network with skip connections (SC-FCN) for end-to-end speech enhancement is proposed. Without the fully connected layers, this network can effectively characterize local information of speech signal, and better restore high frequency components of waveform using lesser number of the parameters. Meanwhile, because of existence of skip connections in different layers, it is easier to train deep networks and the problem of gradient vanishing can also be tackled. In addition, these skip connections can obtain more details of speech signal in different convolutional layers, which is beneficial for recovering the original speech signal. According to our experimental results, the proposed method can recover the waveform better.