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2009
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Acoustic fall detection using Gaussian mixture models and GMM supervectors

10 years 4 months ago
Acoustic fall detection using Gaussian mixture models and GMM supervectors
We present a system that detects human falls in the home environment, distinguishing them from competing noise, by using only the audio signal from a single far-field microphone. The proposed system models each fall or noise segment by means of a Gaussian mixture model (GMM) supervector, whose Euclidean distance measures the pairwise difference between audio segments. A support vector machine built on a kernel between GMM supervectors is employed to classify audio segments into falls and various types of noise. Experiments on a dataset of human falls, collected as part of the Netcarity project, show that the method improves fall classification F-score to 67% from 59% of a baseline GMM classifier. The approach also effectively addresses the more difficult fall detection problem, where audio segment boundaries are unknown. Specifically, we employ it to reclassify confusable segments produced by a dynamic programming scheme based on traditional GMMs. Such post-processing improves a ...
Xiaodan Zhuang, Jing Huang, Gerasimos Potamianos,
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Xiaodan Zhuang, Jing Huang, Gerasimos Potamianos, Mark Hasegawa-Johnson
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