  We present AFEN (Audio Feature Ensemble Learning), a model that leverages
Convolutional Neural Networks (CNN) and XGBoost in an ensemble learning fashion
to perform state-of-the-art audio classification for a range of respiratory
diseases. We use a meticulously selected mix of audio features which provide
the salient attributes of the data and allow for accurate classification. The
extracted features are then used as an input to two separate model classifiers
1) a multi-feature CNN classifier and 2) an XGBoost Classifier. The outputs of
the two models are then fused with the use of soft voting. Thus, by exploiting
ensemble learning, we achieve increased robustness and accuracy. We evaluate
the performance of the model on a database of 920 respiratory sounds, which
undergoes data augmentation techniques to increase the diversity of the data
and generalizability of the model. We empirically verify that AFEN sets a new
state-of-the-art using Precision and Recall as metrics, while decreasing
training time by 60%.
