Real-time Speech Enhancement Using an Efficient Convolutional Recurrent Network for Dual-microphone Mobile Phones in Close-talk Scenarios

Abstract

In mobile speech communication, the quality and intelligibility of the received speech can be severely degraded by background noise if the far-end talker is in an adverse acoustic environment. Therefore, speech enhancement algorithms are typically integrated into mobile phones to remove background noise. In this paper, we propose a novel deep learning based framework for real-time speech enhancement on dual-microphone mobile phones in a close-talk scenario. It incorporates a convolutional recurrent network (CRN) with high computational efficiency. In addition, the framework amounts to a causal system, which is necessary for real-time processing on mobile phones. We find that the proposed approach consistently outperforms a deep neural network (DNN) based method, as well as two traditional methods for speech enhancement.