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.