  Cough is one of the most common symptoms in all respiratory diseases. In
cases like Chronic Obstructive Pulmonary Disease, Asthma, acute and chronic
Bronchitis and the recent pandemic Covid-19, the early identification of cough
is important to provide healthcare professionals with useful clinical
information such as frequency, severity, and nature of cough to enable better
diagnosis. This paper presents and demonstrates best feature selection using
MFCC which can help to determine cough events, eventually helping a neural
network to learn and improve accuracy of cough detection. The paper proposes to
achieve performance of 97.77% Sensitivity (SE), 98.75% Specificity (SP) and
98.17% F1-score with a very light binary classification network of size close
to 16K parameters, enabling fitment into smart IoT devices.
