  Auscultation of respiratory sounds is the primary tool for screening and
diagnosing lung diseases. Automated analysis, coupled with digital
stethoscopes, can play a crucial role in enabling tele-screening of fatal lung
diseases. Deep neural networks (DNNs) have shown a lot of promise for such
problems, and are an obvious choice. However, DNNs are extremely data hungry,
and the largest respiratory dataset ICBHI has only 6898 breathing cycles, which
is still small for training a satisfactory DNN model. In this work, RespireNet,
we propose a simple CNN-based model, along with a suite of novel techniques --
device specific fine-tuning, concatenation-based augmentation, blank region
clipping, and smart padding -- enabling us to efficiently use the small-sized
dataset. We perform extensive evaluation on the ICBHI dataset, and improve upon
the state-of-the-art results for 4-class classification by 2.2%
