  During the outbreak of COVID-19 pandemic, several research areas joined
efforts to mitigate the damages caused by SARS-CoV-2. In this paper we present
an interpretability analysis of a convolutional neural network based model for
COVID-19 detection in audios. We investigate which features are important for
model decision process, investigating spectrograms, F0, F0 standard deviation,
sex and age. Following, we analyse model decisions by generating heat maps for
the trained models to capture their attention during the decision process.
Focusing on a explainable Inteligence Artificial approach, we show that studied
models can taken unbiased decisions even in the presence of spurious data in
the training set, given the adequate preprocessing steps. Our best model has
94.44% of accuracy in detection, with results indicating that models favors
spectrograms for the decision process, particularly, high energy areas in the
spectrogram related to prosodic domains, while F0 also leads to efficient
COVID-19 detection.
