REAL-TIME SPEECH ENHANCEMENT FOR MOBILE COMMUNICATION BASED ON DUAL-CHANNEL COMPLEX SPECTRAL MAPPING

Speech quality and intelligibility can be severely degraded by background noise in mobile communication. In order to attenuate background 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 proposed approach substantially advances the performance of an earlier approach to dual-channel speech enhancement for mobile communication.