# A Progressively Expanded Database for Automated Lung Sound Analysis: An Update
## 1 Introduction  
Respiration is an essential vital sign. Changes in the frequency or intensity of respiratory lung sounds and the manifestation of continuous adventitious sounds (CASs), such as wheezes, stridor, squawk, gasp, and rhonchi, and discontinuous adventitious sounds (DASs), such as crackles and pleural friction rubs, are associated with pulmonary disorders, including pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis [1, 2]. Detection of adventitious sounds during lung sound auscultation helps health-care professionals make clinical decisions. However, the interpretation of lung sounds auscultated with a traditional stethoscope involves inherent subjectivity andrelies on auscultation capability [3]. Electronic auscultation and computerized lung sound analysis can facilitate the interpretation and overcome the above limitations [4]. Computerized lung sound analysis methods based on machine learning have been comprehensively reviewed [5; 6; 7]. Some previous machine learning studies focused on classifying individuals into a healthy group or groups with specific types of respiratory disorders according to their lung sounds. Some studies developed algorithms to classify normal lung sounds and various types of adventitious lung sounds. Some studies proposed methods to detect breath phases and adventitious sounds in lung sound recordings. Over the last few years, more and more researchers have turned their attention to using deep learning for computerized lung sound analysis. Various architectures of deep neural networks (DNN), such as convolutional neural network (CNN) [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18], recurrent neural network [9; 13; 18; 19; 20; 21], residual neural network [22; 23], temporal convolutional network [24], regional CNN [25], and DNN with attention mechanism [26; 27] have been investigated in developing automated lung sound analysis systems. However, many studies were limited by small data volumes, and some datasets used in these studies were private so a comparison between the proposed models cannot be realized. As data size plays an important role to train a more accurate deep learning model, a larger open-access dataset can greatly help the development of lung sound analysis methods. Some public databases of respiratory sound have been established. [PERSON] et al. [28] established an open-access database with which new algorithms for respiratory sound classification can be evaluated and compared. This database, compiled for the first scientific challenge of the International Federation for Medical and Biological Engineering International Conference on Biomedical and Health Informatics (ICBHI) 2017, included 920 audio recordings from 126 subjects and text files showing disease diagnosis and precise location of respiratory cycles, wheezes, and crackles. In addition, [PERSON] and colleagues [29] established a dataset of lung sounds recorded from healthy and unhealthy subjects which included disease labels, such as asthma, heart failure, pneumonia, bronchitis, and COPD. They focused on designing automated machine learning algorithms for the detection of pulmonary disorders. Previously, we reported the development of the world's largest open-access lung sound database, HF_Lung_V1 ([[https://gitlab.com/techsupportHF/HF_Lung_V1](https://gitlab.com/techsupportHF/HF_Lung_V1)]([https://gitlab.com/techsupportHF/HF_Lung_V1](https://gitlab.com/techsupportHF/HF_Lung_V1)), accessed on 18 January 2022). Different from the ICBHI 2017 database, we not only labeled respiratory cycles, wheezes, and crackles, but also labeled inhalations, exhalations, stridor, and rhomchi. However, the HF_Lung_V1 database did not include labels of disease diagnosis. HF_Lung_V1 is benchmarked with several variants of recurrent neural networks [18]. However, while the HF_Lung_V1 database shows high potential for exploiting deep learning for automated inhalation, exhalation, CAS, and DAS detection, it remains to be further improved to achieve better accuracy.