Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction

Interdisciplinary Sciences: Computational Life Sciences - Tập 9 - Trang 550-555 - 2017
Jian Guo1, Kun Qian2, Gongxuan Zhang1, Huijie Xu3, Björn Schuller4
1School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing, China
2Department of Electrical and Computer Engineering, MISP group, MMK Technische University Munchen, Munich, Germany
3Department of Otolaryngology, Beijing Hospital, Beijing, China
4Bjorn Schuller Department of Computing, Machine Learning Group Imperial College London, London, UK

Tóm tắt

The advent of ‘Big Data’ and ‘Deep Learning’ offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for ‘feeding’ the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these ‘big’ data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770–990 MB per subject – in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.

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Interdisciplinary Sciences: Computational Life Sciences

Tập 9

550-555

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