  This paper presents and explores a robust deep learning framework for
auscultation analysis. This aims to classify anomalies in respiratory cycles
and detect disease, from respiratory sound recordings. The framework begins
with front-end feature extraction that transforms input sound into a
spectrogram representation. Then, a back-end deep learning network is used to
classify the spectrogram features into categories of respiratory anomaly cycles
or diseases. Experiments, conducted over the ICBHI benchmark dataset of
respiratory sounds, confirm three main contributions towards respiratory-sound
analysis. Firstly, we carry out an extensive exploration of the effect of
spectrogram type, spectral-time resolution, overlapped/non-overlapped windows,
and data augmentation on final prediction accuracy. This leads us to propose a
novel deep learning system, built on the proposed framework, which outperforms
current state-of-the-art methods. Finally, we apply a Teacher-Student scheme to
achieve a trade-off between model performance and model complexity which
additionally helps to increase the potential of the proposed framework for
building real-time applications.
