Laguarta 2021

DOI: 10.3389/fcomp.2021.624694 One-Liner Proposed a large multimodal approach to embed auditory info + biomarkers for baseline classification. Novelty Developed a massively multimodal audio-to-embedding correlation system that maps audio to biomarker information collected (mood, memory, respiratory) and demonstrated its ability to discriminate cough results for COVID. (they were looking for AD; whoopsies) Notable Methods Developed a feature extraction model for AD detection named Open Voice Brain Model Collected a dataset on people coughing and correlated it with biomarkers Key Figs Figure 2 This is MULTI-MODAL as heck This figure tells us the large network the came up with. Table 2 and 3 The descriminator tacked on the end of the network is transfer-trained to different tasks. It shows promising results for cough-to-COVID classification New Concepts OVBM Lyu 2018 Notes Biomarker correlation Is biomarker data something that is commonly used as a feature extraction/benchmark tool?