  Detecting COVID-19 from audio signals, such as breathing and coughing, can be
used as a fast and efficient pre-testing method to reduce the virus
transmission. Due to the promising results of deep learning networks in
modelling time sequences, and since applications to rapidly identify COVID
in-the-wild should require low computational effort, we present a
temporal-oriented broadcasting residual learning method that achieves efficient
computation and high accuracy with a small model size. Based on the
EfficientNet architecture, our novel network, named Temporal-oriented
ResNet~(TorNet), constitutes of a broadcasting learning block, i.e. the
Alternating Broadcast (AB) Block, which contains several Broadcast Residual
Blocks (BC ResBlocks) and a convolution layer. With the AB Block, the network
obtains useful audio-temporal features and higher level embeddings effectively
with much less computation than Recurrent Neural Networks~(RNNs), typically
used to model temporal information. TorNet achieves 72.2% Unweighted Average
Recall (UAR) on the INTERPSEECH 2021 Computational Paralinguistics Challenge
COVID-19 cough Sub-Challenge, by this showing competitive results with a higher
computational efficiency than other state-of-the-art alternatives.
