Transfer Learning and Data Augmentation Techniques to the COVID-19 Identification Tasks in ComParE 2021

Autor(es)

Casanova, Edresson ; Candido Jr, Arnaldo ; Fernandes Jr, Ricardo Corso ; Finger, Marcelo ; Stefanel Gris, Lucas Rafael ; Ponti, Moacir A. ; Pinto da Silva, Daniel Peixoto ; Int Speech Commun Assoc

• Tipo de documento: Artigo Científico
• Fonte: INTERSPEECH 2021; v. N/A, p. 5-pg., 2021-01-01.

Resumo

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). (AU)

• Processo FAPESP: 20/06443-5 - Estudo Spira: sistema de detecção precoce de insuficiência respiratória por meio de análise de áudio
• Beneficiário: Marcelo Finger
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia