A Temporal-oriented Broadcast ResNet for COVID-19 Detection

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, 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 Temporaloriented ResNet (TorNet), constitutes of a broadcasting learning 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. © 2022 IEEE.

Coughing-based Recognition of Covid-19 with Spatial Attentive ConvLSTM Recurrent Neural Networks

The rapid emergence of COVID-19 has become a major public health threat around the world. Although early detection is crucial to reduce its spread, the existing diagnostic methods are still insufficient in bringing the pandemic under control. Thus, more sophisticated systems, able to easily identify the infection from a larger variety of symptoms, such as cough, are urgently needed. Deep learning models can indeed convey numerous signal features relevant to fight against the disease;yet, the performance of state-of-the-art approaches is still severely restricted by the feature information loss typically due to the high number of layers. To mitigate this phenomenon, identifying the most relevant feature areas by drawing into attention mechanisms becomes essential. In this paper, we introduce Spatial Attentive ConvLSTM-RNN (SACRNN), a novel algorithm that is using Convolutional Long-Short Term Memory Recurrent Neural Networks with embedded attention that has the ability to identify the most valuable features. The promising results achieved by the fusion between the proposed model and a conventional Attentive Convolutional Recurrent Neural Network, on the automatic recognition of COVID-19 coughing (73.2 % of Unweighted Average Recall) show the great potential of the presented approach in developing efficient solutions to defeat the pandemic.

CONVOLUATIONAL TRANSFORMER WITH ADAPTIVE POSITION EMBEDDING FOR COVID-19 DETECTION FROM COUGH SOUNDS

Covid-19 has caused a huge health crisis worldwide in the past two years. Although an early detection of the virus through nucleic acid screening can considerably reduce its spread, the efficiency of this diagnostic process is limited by its complexity and costs. Hence, an effective and inexpensive way to early detect Covid-19 is still needed. Considering that the cough of an infected person contains a large amount of information, we propose an algorithm for the automatic recognition of Covid-19 from cough signals. Our approach generates static log-Mel spectrograms with deltas and delta-deltas from the cough signal and subsequently extracts feature maps through a Convolutional Neural Network (CNN). Following the advances on transformers in the realm of deep learning, our proposed architecture exploits a novel adaptive position embedding structure which can learn the position information of the features from the CNN output. This make the transformer structure rapidly lock the attention feature location by overlaying with the CNN output, which yields better classification. The efficiency of the proposed architecture is shown by the improvement, w. r. t. the baseline, of our experimental results on the INTERPSEECH 2021 Computational Paralinguistics Challenge CCS (Coughing Sub Challenge) database, which reached 72.6 % UAR (Unweighted Average Recall). © 2022 IEEE