Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909212069593088 |
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| author | Gauy, Marcelo Matheus Berti, Larissa Cristina Cândido Jr, Arnaldo Neto, Augusto Camargo Goldman, Alfredo Levin, Anna Sara Shafferman Martins, Marcus de Medeiros, Beatriz Raposo Queiroz, Marcelo Sabino, Ester Cerdeira Svartman, Flaviane Romani Fernandes Finger, Marcelo |
| author_facet | Gauy, Marcelo Matheus Berti, Larissa Cristina Cândido Jr, Arnaldo Neto, Augusto Camargo Goldman, Alfredo Levin, Anna Sara Shafferman Martins, Marcus de Medeiros, Beatriz Raposo Queiroz, Marcelo Sabino, Ester Cerdeira Svartman, Flaviane Romani Fernandes Finger, Marcelo |
| contents | This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during the first phase of the pandemic and trained modern AI models, such as CNNs and Transformers, which achieved $96.5\%$ accuracy, showing the feasibility of RI detection via AI. Here, we collect RI patient data (P2) with several causes besides COVID-19, aiming at extending AI-based RI detection. We also collected control data from hospital patients without RI. We show that the considered models, when trained on P1, do not generalize to P2, indicating that COVID-19 RI has features that may not be found in all RI types. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17569 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese Gauy, Marcelo Matheus Berti, Larissa Cristina Cândido Jr, Arnaldo Neto, Augusto Camargo Goldman, Alfredo Levin, Anna Sara Shafferman Martins, Marcus de Medeiros, Beatriz Raposo Queiroz, Marcelo Sabino, Ester Cerdeira Svartman, Flaviane Romani Fernandes Finger, Marcelo Machine Learning Artificial Intelligence Sound Audio and Speech Processing This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during the first phase of the pandemic and trained modern AI models, such as CNNs and Transformers, which achieved $96.5\%$ accuracy, showing the feasibility of RI detection via AI. Here, we collect RI patient data (P2) with several causes besides COVID-19, aiming at extending AI-based RI detection. We also collected control data from hospital patients without RI. We show that the considered models, when trained on P1, do not generalize to P2, indicating that COVID-19 RI has features that may not be found in all RI types. |
| title | Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese |
| topic | Machine Learning Artificial Intelligence Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2405.17569 |