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For dual-channel speech enhancement, it is a promising idea to design anend-to-end model based on the traditional array signal processing guideline andthe manifold space of multi-channel signals. We found that the idea above canbe effectively implemented by the classical convolutional recurrent neuralnetworks (CRN) architecture. We propose a very compact in place gatedconvolutional recurrent neural network (inplace GCRN) for end-to-endmulti-channel speech enhancement, which utilizes inplace-convolution forfrequency pattern extraction and reconstruction. The inplace characteristicsefficiently preserve spatial cues in each frequency bin for channel-wise longshort-term memory neural networks (LSTM) tracing the spatial source. Inaddition, we come up with a new spectrum recovery method by predict amplitudemask, mapping, and phase, which effectively improves the speech quality.

Inplace Gated Convolutional Recurrent Neural Network For Dual-channel Speech Enhancement