Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement

Understand

The capability of the human to pay attention to both coarse and fine-grained regions has been applied to computer vision tasks.

• Motivated by that, we propose a collaborative learning framework in the complex domain for monaural noise suppression.
• The proposed system consists of two principal modules, namely spectral feature extraction module (FEM) and stacked glance-gaze modules (GGMs).
• In FEM, the UNet-block is introduced after each convolution layer, enabling the feature recalibration from multiple scales.