  In this paper, we evaluate various deep learning frameworks for detecting
respiratory anomalies from input audio recordings. To this end, we firstly
transform audio respiratory cycles collected from patients into spectrograms
where both temporal and spectral features are presented, referred to as the
front-end feature extraction. We then feed the spectrograms into back-end deep
learning networks for classifying these respiratory cycles into certain
categories. Finally, results from high-performed deep learning frameworks are
fused to obtain the best score. Our experiments on ICBHI benchmark dataset
achieve the highest ICBHI score of 57.3 from a late fusion of inception based
and transfer learning based deep learning frameworks, which outperforms the
state-of-the-art systems.
