# Respiratory Sound Classification by Applying Deep Neural Network with a Blocking Variable
## 1 Introduction  
Respiratory diseases ranked the fourth leading cause of death worldwide by the world health organization ([[https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death](https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death)]([https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death](https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death)) (accessed on 9 December 2020)). Typical respiratory diseases include asthma, chronic obstructive pulmonary disease (COPD), and lower respiratory tract infection. These diseases can threaten people's lives if treatment is not provided on time. Thus, the early detection of the diseases is crucial. However, the resources of experienced doctors are limited. Through [PERSON]'s research [1], he discovered hospital trainees and medical students made the wrong diagnosis on almost half of the pulmonary sounds. With the outbreak of COVID-19, the lack of medical resources is even more acute [2]. The prevalence of respiratory diseases has increased due to the long-term sequelae resulting from COVID-19 [3]. Thus, it is meaningful to develop an automatic respiratory disease detection system. The system can help improve the correct diagnosis of respiratory diseases for hospital trainees and enable the patients to have a quick diagnosis anywhere, even at home. In general, a respiratory sound could be classified as a normal or adventitious sound. Crackles and wheezees are two of the most common adventitious sounds. A wheeze is a continuous adventitious sound, which is caused by the limitation of the airflow due to the tightening of the airway [4]. Wheezing may occur at either the inspiration stage or the expiration stage, but mostly at the expiration stage. A wheeze is a high-pitch sound, and its frequency range is generally between 100 and1000 Hz, mostly larger than 400 Hz, and its minimum duration is between 80 ms and 100 ms [5]. The most common diseases associated with wheezing are asthma and COPD. A crackle is a discontinuous sound, which is caused by air bubbles in the bronchi, and it is a short, explosive sound [6]. The sounds can befound mostly at the early stages of inspiration but are also audible at the expiatory stage. A crackle is a low-pitch sound, and its frequency is between 100 and 500 Hz, mostly at 350 Hz, with a duration usually around 15 ms [7]. The most common diseases associated with a crackle sound is chronic bronchitis, bronchiectasis, and COPD. In addition, the lungs are much larger than the heart, so the respiratory sounds should be recorded at multiple sites of both lungs for an accurate diagnosis. Those positions are the trachea, anterior, posterior and lateral. The ICBHI 2017 database [8] is the largest open respiratory sound database published by the International Conference on Biomedical Health Informatics (ICBHI), which has attracted numerous teams to develop their own automatic respiratory sound classificaition system. In the previous research, both traditional machine learning and deep learning fields have published a large number of papers to tackle the classification task of the ICBHI 2017 respiratory sound database. These authors proposed three different traditional machine learning methods, including the Hidden Markov Model with the Gaussian Mixture model [9], Boosting Decision Tree [10], and Support Vector Machine [11], to conduct the classification. With the rise of computation power, deep learning became popular and achieves a better performance. The authors of [12] used the deep recurrent neural network with a noise masking model, [PERSON]. [13] introduced a hybrid CNN-RNN and patient-specific model, and [PERSON] et al. [14] proposed a Bi-resnet to implement the classification. [PERSON]. [15] developed a lung network with a non-local layer, and proposed a special mix-up data augmentation to increase the data size. [PERSON]. [16] introduced device-specific fine-tuning and also mix-up data augmentation to improve the performance of their Respire network.