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Audio for Audio is Better? An Investigation on Transfer Learning Models for Heart Sound Classification

Koike, Tomoya, Kong, Qiuqiang and Plumbley, Mark (2020) Audio for Audio is Better? An Investigation on Transfer Learning Models for Heart Sound Classification In: 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 20-24 July 2020, Virtual Conference.

KoikeEtAl20-embc_accepted.pdf - Accepted version Manuscript

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Cardiovascular disease is one of the leading factors for death cause of human beings. In the past decade, heart sound classification has been increasingly studied for its feasibility to develop a non-invasive approach to monitor a subject’s health status. Particularly, relevant studies have benefited from the fast development of wearable devices and machine learning techniques. Nevertheless, finding and designing efficient acoustic properties from heart sounds is an expensive and time-consuming task. It is known that transfer learning methods can help extract higher representations automatically from the heart sounds without any human domain knowledge. However, most existing studies are based on models pre-trained on images, which may not fully represent the characteristics inherited from audio. To this end, we propose a novel transfer learning model pretrained on large scale audio data for a heart sound classification task. In this study, the PhysioNet CinC Challenge Dataset is used for evaluation. Experimental results demonstrate that, our proposed pre-trained audio models can outperform other popular models pre-trained by images by achieving the highest unweighted average recall at 89.7 %.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Koike, Tomoya
Kong, Qiuqiang
Date : 10 April 2020
Additional Information : Embargo OK Metadata Pending
Depositing User : James Marshall
Date Deposited : 06 Aug 2020 14:03
Last Modified : 06 Aug 2020 14:03

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