# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 1 Introduction  
The most important information about the respiratory system can be obtained from the cough sounds of a particular patient [1]. The cough sounds of every respiratory disease are quite unique to each other, and so, the diagnosis of it can be made easily by the physician. The expelled air from the lungs is reflected by the waveforms of the cough sounds. The cough has a specific sound pressure, and its unique relationship to the respiratory condition of the patient can be demonstrated well [2]. The esophageal pressure and the cough flow have a good relationship with the power of cough sounds. The voiced phase, explosive phase and intermediate phase are the three main phases of the individual cough sounds. When COVID-19 occurred, Artificial Intelligence (AI) techniques helped a lot in the diagnosis of various medical conditions and provided good solutions for patient outcome prediction depending on various inputs of the data [3]. Machine learning algorithms with AI techniques helped a lot in developing a quick and inexpensive approach to diagnosing COVID-19 problems. The recording of human audio signals can be performed in an instantaneous manner. With the advent of cough segmentation, data segmentation, machine learning and deep learning techniques, audio signal classificationhas been performed in the past decade. A few important works conducted in the past decade and in the past few years include the following. For detecting abnormal pulmonary function, the classification of voluntary cough sounds and airflow patterns was conducted by [PERSON] et al. (2018). By internal sound analysis, the detection of voluntary cough sounds was implemented by [PERSON] et al. (2018). Asthmatic cough sounds were described quantitatively by [PERSON] et al. (2018). For the rapid diagnosis of childhood pneumonia, wavelet-augmented cough analysis was examined by [PERSON] et al. (2018). Cough sound recognition was used for automatic group diagnosis by [PERSON] et al. (2018). Wet and dry coughs in pediatric patients were automatically identified and classified by [PERSON] et al. (2018). The audio-based assessment of cough analysis was performed by [PERSON] et al. (2018). The analysis of cough sounds with objective correlation and spirometry was conducted by [PERSON] et al., where an accuracy of 91.97% was obtained ([PERSON] et al., 2018). A multi-modal deep learning method for the analysis of cough sounds was presented by [PERSON] et al., where an accuracy of 99.01% was obtained ([PERSON] et al., 2018). In a real-world environment, automatic cough classification for tuberculosis diagnosis was performed by [PERSON] et al. (2018). The Mel Frequency Cepstral Coefficient (MFCC) features and a CNN were used for cough classification for COVID-19 by [PERSON] et al. (2018). A multi-branch deep learning model for COVID-19 detection from cough sounds was developed by [PERSON] et al. (2018). Scalogram image specification using deep learning models was performed for cough audio classification by [PERSON] et al. (2018). An ensemble approach for detecting COVID-19 from cough sounds was developed by [PERSON] et al. (2018). The unique spectral fingerprints were identified in cough sounds for diagnosing respiratory ailments by [PERSON] et al. (2018). A deep neural network-based respiratory pathology classification using cough sounds was conducted by [PERSON] et al. (2018). A Malaysian cough sound analysis and COVID-19 classification with deep learning was conducted by [PERSON] et al. (2018). With the help of interpretable symptom embeddings, the early diagnosis of COVID-19 was performed by [PERSON] et al. (2018). Cough classification using machine learning and global smartphone recordings was conducted by [PERSON] et al. (2018). An in-depth study of utilizing cough sounds and deep neural networks for the early detection of COVID-19 was conducted by [PERSON] et al. (2018). The wavelet analysis of voluntary cough sounds in patients with respiratory diseases was performed by [PERSON] et al. (2018).