Deep Multivariate Domain Translation for Device Invariant Pulmonary Patient Identification from Cough and Speech Sounds
Engineering in Medicine and Biology Conference (EMBC)
audio based machine learning models to infer pulmonary health, exacerbation and activity. A major challenge to widespread usage and deployment of such pulmonary health monitoring audio models is to maintain accuracy and robustness across a variety of commodity devices, due to the effect of device heterogeneity. Because of this phenomenon, pulmonary audio models developed with data from one type of device perform poorly when deployed on another type of device. In this work, we propose a framework incorporating a multivariate deep neural network regressor as a feature translator from the source device domain to the target device domain. Our empirical and extensive experiments with data from 131 real pulmonary patients and healthy controls show that our framework can recover upto 66.67% of the accuracy lost due to device heterogeneity for two different pulmonary activity based person identification tasks with two common mobile and wearable devices: smartphone and smartwatch.