Multi-Microphone Complex Spectral Mapping for Speech Dereverberation
Zhong-Qiu Wang, DeLiang Wang
Abstract
This study proposes a multi-microphone complex spectral mapping approach for speech dereverberation on a fixed array geometry. In the proposed approach, a deep neural network (DNN) is trained to predict the real and imaginary (RI) components of direct sound from the stacked reverberant (and noisy) RI components of multiple microphones. We also investigate the integration of multi-microphone complex spectral mapping with beamforming and post-filtering. Experimental results on multi-channel speech dereverberation demonstrate the effectiveness of the proposed approach.
Topics & Concepts
MicrophoneBeamformingComputer scienceSpeech recognitionSpeech enhancementMicrophone arrayNoise-canceling microphoneArtificial neural networkSpeech processingAcousticsArtificial intelligenceNoise reductionTelecommunicationsPhysicsSound pressureSpeech and Audio ProcessingAdvanced Adaptive Filtering TechniquesHearing Loss and Rehabilitation