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基于双声道音频的远程监测中稳健的日常生活活动分类声音

Robust sounds of activities of daily living classification in two-channel audio-based telemonitoring.

作者信息

Maunder David, Epps Julien, Ambikairajah Eliathamby, Celler Branko

机构信息

School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia.

出版信息

Int J Telemed Appl. 2013;2013:696813. doi: 10.1155/2013/696813. Epub 2013 Apr 22.

DOI:10.1155/2013/696813
PMID:23710171
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3654347/
Abstract

Despite recent advances in the area of home telemonitoring, the challenge of automatically detecting the sound signatures of activities of daily living of an elderly patient using nonintrusive and reliable methods remains. This paper investigates the classification of eight typical sounds of daily life from arbitrarily positioned two-microphone sensors under realistic noisy conditions. In particular, the role of several source separation and sound activity detection methods is considered. Evaluations on a new four-microphone database collected under four realistic noise conditions reveal that effective sound activity detection can produce significant gains in classification accuracy and that further gains can be made using source separation methods based on independent component analysis. Encouragingly, the results show that recognition accuracies in the range 70%-100% can be consistently obtained using different microphone-pair positions, under all but the most severe noise conditions.

摘要

尽管家庭远程监测领域最近取得了进展，但使用非侵入性且可靠的方法自动检测老年患者日常生活活动的声音特征这一挑战依然存在。本文研究了在现实嘈杂环境下，从任意放置的双麦克风传感器对八种典型日常生活声音进行分类的情况。特别地，考虑了几种源分离和声音活动检测方法的作用。对在四种现实噪声条件下收集的新四麦克风数据库进行的评估表明，有效的声音活动检测可显著提高分类准确率，并且使用基于独立成分分析的源分离方法可进一步提高准确率。令人鼓舞的是，结果表明，除了最恶劣的噪声条件外，在所有情况下使用不同麦克风对位置均可始终如一地获得70% - 100%的识别准确率。

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本文引用的文献

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The sweet-home project: audio technology in smart homes to improve well-being and reliance.甜蜜家园项目：智能家居中的音频技术，以改善幸福感和依赖感。
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5291-4. doi: 10.1109/IEMBS.2011.6091309.
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SVM-based multimodal classification of activities of daily living in Health Smart Homes: sensors, algorithms, and first experimental results.基于支持向量机的健康智能家居中日常生活活动多模态分类：传感器、算法及初步实验结果
IEEE Trans Inf Technol Biomed. 2010 Mar;14(2):274-83. doi: 10.1109/TITB.2009.2037317. Epub 2009 Dec 11.
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Dual-microphone Sounds of Daily Life classification for telemonitoring in a noisy environment.
Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:4636-9. doi: 10.1109/IEMBS.2008.4650246.
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IEEE Trans Inf Technol Biomed. 2006 Apr;10(2):264-74. doi: 10.1109/titb.2005.859889.
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