Transfer Learning and Data Augmentation Techniques to the COVID-19 Identification Tasks in ComParE 2021
(Oral presentation)
| Edresson Casanova (Universidade de São Paulo, Brazil), Arnaldo Candido Jr. (Universidade Tecnológica Federal do Paraná, Brazil), Ricardo Corso Fernandes Jr. (Universidade Tecnológica Federal do Paraná, Brazil), Marcelo Finger (Universidade de São Paulo, Brazil), Lucas Rafael Stefanel Gris (Universidade Tecnológica Federal do Paraná, Brazil), Moacir Antonelli Ponti (Universidade de São Paulo, Brazil), Daniel Peixoto Pinto da Silva (Universidade Tecnológica Federal do Paraná, Brazil) |
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In this work, we propose several techniques to address data scarceness in ComParE 2021 COVID-19 identification tasks for the application of deep models such as Convolutional Neural Networks. Data is initially preprocessed into spectrogram or MFCC-gram formats. After preprocessing, we combine three different data augmentation techniques to be applied in model training. Then we employ transfer learning techniques from pretrained audio neural networks. Those techniques are applied to several distinct neural architectures. For COVID-19 identification in speech segments, we obtained competitive results. On the other hand, in the identification task based on cough data, we succeeded in producing a noticeable improvement on existing baselines, reaching 75.9% unweighted average recall (UAR).
