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An unsupervised acoustic fall detection system using source separation for sound interference suppression
journal contribution
posted on 2014-12-22, 10:42 authored by Muhammad Salman Khan, Miao Yu, Pengming Feng, Liang Wang, Jonathon ChambersWe present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person׳s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible interferences from other background sound sources. Mel-frequency cepstral coefficient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is finally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset confirm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods.
Funding
M.S. Khan acknowledges the financial support of UET, Peshawar, the Higher Education Commission (HEC) of Pakistan and Engineering and Physical Sciences Research Council (EPSRC), [Grant no. EP/K014307/1].
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
Signal ProcessingVolume
110Pages
199 - 210Citation
KHAN, M.S. ... et al, 2015. An unsupervised acoustic fall detection system using source separation for sound interference suppression. Signal Processing, 110, pp.199-210.Publisher
Crown Copyright © Published by Elsevier B.V.Version
- VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/Acceptance date
2014-08-12Publication date
2014-08-27Notes
This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/ISSN
0165-1684Publisher version
Language
- en