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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 modelperformance. Moreover, the issues of noisy labels and sound overlapping wereexplored. Methods: HF_Lung_V1 was expanded to HF_Lung_V2 with a 1.45x increasein the number of audio files. Convolutional neural network-bidirectional gatedrecurrent 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 theHF_Lung_V1 (V1_Test) and HF_Lung_V2 (V2_Test) test sets, respectively. Segmentand event detection performance was evaluated using the F1 scores. Labelquality was assessed. Moreover, the overlap ratios between inhalation,exhalation, CAS, and DAS labels were computed. Results: The model trained usingV2_Train exhibited improved F1 scores in inhalation, exhalation, and CASdetection on both V1_Test and V2_Test but not in DAS detection. Poor CASdetection was attributed to the quality of CAS labels. DAS detection wasstrongly influenced by the overlapping of DAS labels with inhalation andexhalation labels. Conclusion: Collecting greater quantities of lung sound datais vital for developing more accurate lung sound analysis models. To build realground-truth labels, the labels must be reworked; this process is ongoing.Furthermore, a method for addressing the sound overlapping problem in DASdetection must be formulated.

An Update on a Progressively Expanded Database for Automated Lung Sound Analysis