  The research direction of identifying acoustic bio-markers of respiratory
diseases has received renewed interest following the onset of COVID-19
pandemic. In this paper, we design an approach to COVID-19 diagnostic using
crowd-sourced multi-modal data. The data resource, consisting of acoustic
signals like cough, breathing, and speech signals, along with the data of
symptoms, are recorded using a web-application over a period of ten months. We
investigate the use of statistical descriptors of simple time-frequency
features for acoustic signals and binary features for the presence of symptoms.
Unlike previous works, we primarily focus on the application of simple linear
classifiers like logistic regression and support vector machines for acoustic
data while decision tree models are employed on the symptoms data. We show that
a multi-modal integration of acoustics and symptoms classifiers achieves an
area-under-curve (AUC) of 92.40, a significant improvement over any individual
modality. Several ablation experiments are also provided which highlight the
acoustic and symptom dimensions that are important for the task of COVID-19
diagnostics.
