  Respiratory auscultation can help healthcare professionals detect abnormal
respiratory conditions if adventitious lung sounds are heard. The
state-of-the-art artificial intelligence technologies based on deep learning
show great potential in the development of automated respiratory sound
analysis. To train a deep learning-based model, a huge number of accurate
labels of normal breath sounds and adventitious sounds are needed. In this
paper, we demonstrate the work of developing a respiratory sound labeling
software to help annotators identify and label the inhalation, exhalation, and
adventitious respiratory sound more accurately and quickly. Our labeling
software integrates six features from MATLAB Audio Labeler, and one commercial
audio editor, RX7. As of October, 2019, we have labeled 9,765 15-second-long
audio files of breathing lung sounds, and accrued 34,095 inhalation
labels,18,349 exhalation labels, 13,883 continuous adventitious sounds (CASs)
labels and 15,606 discontinuous adventitious sounds (DASs) labels, which are
significantly larger than previously published studies. The trained
convolutional recurrent neural networks based on these labels showed good
performance with F1-scores of 86.0% on inhalation event detection, 51.6% on
CASs event detection and 71.4% on DASs event detection. In conclusion, our
results show that our proposed respiratory sound labeling software could easily
pre-define a label, perform one-click labeling, and overall facilitate the
process of accurately labeling. This software helps develop deep learning-based
models that require a huge amount of labeled acoustic data.
