  Hand-crafted spatial features, such as inter-channel intensity difference
(IID) and inter-channel phase difference (IPD), play a fundamental role in
recent deep learning based dual-microphone speech enhancement (DMSE) systems.
However, learning the mutual relationship between artificially designed spatial
and spectral features is hard in the end-to-end DMSE. In this work, a novel
architecture for DMSE using a multi-head cross-attention based convolutional
recurrent network (MHCA-CRN) is presented. The proposed MHCA-CRN model includes
a channel-wise encoding structure for preserving intra-channel features and a
multi-head cross-attention mechanism for fully exploiting cross-channel
features. In addition, the proposed approach specifically formulates the
decoder with an extra SNR estimator to estimate frame-level SNR under a
multi-task learning framework, which is expected to avoid speech distortion led
by end-to-end DMSE module. Finally, a spectral gain function is adopted to
further suppress the unnatural residual noise. Experiment results demonstrated
superior performance of the proposed model against several state-of-the-art
models.
