  Smartphones and wearable devices, along with Artificial Intelligence, can
represent a game-changer in the pandemic control, by implementing low-cost and
pervasive solutions to recognize the development of new diseases at their early
stages and by potentially avoiding the rise of new outbreaks. Some recent works
show promise in detecting diagnostic signals of COVID-19 from voice and coughs
by using machine learning and hand-crafted acoustic features. In this paper, we
decided to investigate the capabilities of the recently proposed deep embedding
model L3-Net to automatically extract meaningful features from raw respiratory
audio recordings in order to improve the performances of standard machine
learning classifiers in discriminating between COVID-19 positive and negative
subjects from smartphone data. We evaluated the proposed model on 3 datasets,
comparing the obtained results with those of two reference works. Results show
that the combination of L3-Net with hand-crafted features overcomes the
performance of the other works of 28.57% in terms of AUC in a set of
subject-independent experiments. This result paves the way to further
investigation on different deep audio embeddings, also for the automatic
detection of different diseases.
