  This paper proposes a robust deep learning framework used for classifying
anomaly of respiratory cycles. Initially, our framework starts with front-end
feature extraction step. This step aims to transform the respiratory input
sound into a two-dimensional spectrogram where both spectral and temporal
features are well presented. Next, an ensemble of C- DNN and Autoencoder
networks is then applied to classify into four categories of respiratory
anomaly cycles. In this work, we conducted experiments over 2017 Internal
Conference on Biomedical Health Informatics (ICBHI) benchmark dataset. As a
result, we achieve competitive performances with ICBHI average score of 0.49,
ICBHI harmonic score of 0.42.
