Real-Time Speech Enhancement for Mobile Communication Based on Dual-Channel Complex Spectral Mapping

Speech quality and intelligibility can be severely degraded by back-ground noise in mobile communication. In order to attenuate back-ground noise, speech enhancement systems have been integrated into mobile phones, and a microphone array is typically deployed to improve the enhancement performance. This paper proposes a novel approach to real-time speech enhancement for dual-microphone mobile phones. Our approach employs a causal densely-connected convolutional recurrent network to perform dual-channel complex spectral mapping. We apply a structured pruning technique for compressing the model without significantly affecting the enhancement performance. This leads to a real-time enhancement system for on-device processing. Evaluation results show that the pro-posed approach substantially advances the performance of an earlier approach to dual-channel speech enhancement for mobile communication.