A glance-and-gaze network for respiratory sound classification

A plethora of great successes has been achieved by the existing convolutional neural networks (CNN) for respiratory sound classification. Nevertheless, simultaneously capturing both the local and global features can never be an easy task due to the limitation of a CNN's structure. In this contribution, we propose a novel glance-and-gaze network to address the aforementioned issue. The glance block aims to learn global information, while the gaze block is responsible for learning local patterns and suppressing the noises that attenuates the final performance. In the proposed method, both the global and local information can be extracted. Moreover, the spectral and temporal representations can be learnt via a feature fusion module. Experimental results on the largest public respiratory sound database demonstrate that the proposed model outperforms the state-of-the-art methods.