  Purpose: We previously established an open-access lung sound database,
HF_Lung_V1, and developed deep learning models for inhalation, exhalation,
continuous adventitious sound (CAS), and discontinuous adventitious sound (DAS)
detection. The amount of data used for training contributes to model accuracy.
Herein, we collected larger quantities of data to further improve model
performance. Moreover, the issues of noisy labels and sound overlapping were
explored. Methods: HF_Lung_V1 was expanded to HF_Lung_V2 with a 1.45x increase
in the number of audio files. Convolutional neural network-bidirectional gated
recurrent unit network models were trained separately using the HF_Lung_V1
(V1_Train) and HF_Lung_V2 (V2_Train) training sets and then tested using the
HF_Lung_V1 (V1_Test) and HF_Lung_V2 (V2_Test) test sets, respectively. Segment
and event detection performance was evaluated using the F1 scores. Label
quality was assessed. Moreover, the overlap ratios between inhalation,
exhalation, CAS, and DAS labels were computed. Results: The model trained using
V2_Train exhibited improved F1 scores in inhalation, exhalation, and CAS
detection on both V1_Test and V2_Test but not in DAS detection. Poor CAS
detection was attributed to the quality of CAS labels. DAS detection was
strongly influenced by the overlapping of DAS labels with inhalation and
exhalation labels. Conclusion: Collecting greater quantities of lung sound data
is vital for developing more accurate lung sound analysis models. To build real
ground-truth labels, the labels must be reworked; this process is ongoing.
Furthermore, a method for addressing the sound overlapping problem in DAS
detection must be formulated.

Sound;Artificial Intelligence;Machine Learning;Audio and Speech Processing