  Many deep learning-based computerized respiratory sound analysis methods have
previously been developed. However, these studies focus on either lung sound
only or tracheal sound only. The effectiveness of using a lung sound analysis
algorithm on tracheal sound and vice versa has never been investigated.
Furthermore, no one knows whether using lung and tracheal sounds together in
training a respiratory sound analysis model is beneficial. In this study, we
first constructed a tracheal sound database, HF_Tracheal_V1, containing 10448
15-s tracheal sound recordings, 21741 inhalation labels, 15858 exhalation
labels, and 6414 continuous adventitious sound (CAS) labels. HF_Tracheal_V1 and
our previously built lung sound database, HF_Lung_V2, were either combined
(mixed set), used one after the other (domain adaptation), or used alone to
train convolutional neural network bidirectional gate recurrent unit models for
inhalation, exhalation, and CAS detection in lung and tracheal sounds. The
results revealed that the models trained using lung sound alone performed
poorly in tracheal sound analysis and vice versa. However, mixed set training
or domain adaptation improved the performance for 1) inhalation and exhalation
detection in lung sounds and 2) inhalation, exhalation, and CAS detection in
tracheal sounds compared to positive controls (the models trained using lung
sound alone and used in lung sound analysis and vice versa). In particular, the
model trained on the mixed set had great flexibility to serve two purposes,
lung and tracheal sound analyses, at the same time.
