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首页> 外文期刊>International journal of telemedicine and applications >Robust Sounds of Activities of Daily Living Classification in Two-Channel Audio-Based Telemonitoring
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Robust Sounds of Activities of Daily Living Classification in Two-Channel Audio-Based Telemonitoring

机译：两通道基于音频的远程监控中日常生活分类活动的强劲声音

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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.
机译：尽管在家庭远程监控领域中最近取得了进步，但是使用非侵入性和可靠的方法来自动检测老年患者的日常生活活动的声音信号的挑战仍然存在。本文研究了在现实的嘈杂条件下，任意放置的两个麦克风传感器对日常生活中八种典型声音的分类。特别是，考虑了几种音源分离和声音活动检测方法的作用。对在四个实际噪声条件下收集的新的四麦克风数据库进行的评估表明，有效的声音活动检测可以显着提高分类精度，并且可以使用基于独立成分分析的信号源分离方法来进一步提高增益。令人鼓舞的是，结果表明，除了最严重的噪声条件外，使用不同的麦克风对位置可以始终获得70％–100％的识别精度。

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