## 1 Introduction  
Cough in children is one of the commonest presentations to medical practitioners [1; 2; 3; 4; 5; 6; 7; 8; 9]. In children, cough is a manifestation of a broad spectrum of respiratory illnesses, ranging from rhinosinusitis, atopy, respiratory tract infection, and inhaled foreign body, to post viral/infectious cough [1; 2; 10]. In particular, cough is a major symptom associated with asthma [7; 8; 11]. Cough alone can also be the only manifestation of cough variant asthma [6; 12; 13], a leading cause of chronic cough in children [6; 7; 12]. At the same time, cough can also be benign and healthy children might have up to 100 epochs of cough in 24 h [14]. Despite its prevalence and its recognition as a cardinal symptom in asthma, the clinical value of cough is limited by the lack of available validated objective measures of cough sound characteristics, as well as poor reliability and reproducibility [15] of describing cough in a patient's history. Thus, the clinical diagnosis, assessment of severity, and the progress monitoring of asthma remain a clinical challenge. It has been recognized that certain unique cough characteristics in children can be of diagnostic value [16], such as the classical barking sound for group, paroxysmal spasmodic whoop of pertussis, dry staccato for chlamydia [3; 16], and brassy cough for tracheomalacia [5]. However, the sensitivity and specificity of many cough sound qualities have not been defined or studied extensively [1; 16]. There is a growing interest in using acoustic features to objectively classify cough sounds from different respiratory diseases, although most work is still fairly limited. Progress in automated cough sound classification systems over the years include systems that differentiate between productive and non-productive cough [17], which detect abnormal lung function [18] and enable rapid diagnosis of pneumonia [19]. Particularly for asthma, cough sound spectrograms were first studied in 1989 by [PERSON] [20], where he compared nocturnal and post exercise cough sounds in one 7-year-old child with asthma with those of a normal child--demonstrating a characteristic frequency spectrum for asthmatic coughs. In another study with 24 children aged 4-11 years old, [PERSON] [21] examined time-frequency domain features (wavelet packet decomposition, Wigner distribution) from cough sounds recorded in a noise-free environment, to differentiate between asthmatic and non-asthmatic coughs, yielding 88% and 100% agreement, respectively, with physician diagnosis, when verified with eight subjects. There was no mention whether the subjects tested were from the training cohort; demographic details (other than sex and age), clinical presentations, and clinical history of the recruited children were also unreported. In another study, [PERSON] [22] studied cough sounds from 18 children (9 asthma, 9 pneumonia; aged 1 to 86 months) using an artificial neural network to differentiate between the two pathologies, through multiple audio features (Mel-Frequency Cepstral Coefficients (MFCCs), non-Gaussian score, and Shannon entropy), and achieved a sensitivity, specificity, and kappa of 89%, 100%, and 0.89, respectively. Although these earlier studies were limited by a fairly small sample size and a limited scope of investigation, and required sophisticated audio recording setups, it was evident that meaningful acoustic parameters could be extracted from cough sounds associated with asthma and could be used as an objective method for assessing asthma. Leveraging on the advancements in audio signal processing, machine learning, and the ubiquitous use of smartphones, we aimed here to develop an audio-based machine learning solution to automatically differentiate pathological pulmonary cough (asthmatic) from normal-voluntary coughs. To achieve this, we built a clinical database of voluntary cough sounds from children with and without pathological respiratory conditions. These cough sounds were recorded using smartphones.