A real-time unusual voice detector based on nursing at home

In this paper, we will propose a method to detect an unusual voice for nursing system. Based on the healthy condition of a person, we define four kinds of unusual voices including cough, groan, wheeze and cry for help. When the person nursed sends out the unusual voices, we judge that his health con...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 4; pp. 2368 - 2373
Main Authors Min-Quan Jing, Chao-Chun Wang, Ling-Hwei Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Costs
Cough
Cry for help
Cybernetics
Detectors
Feature extraction
Frequency
groan
Machine learning
Medical services
Multiple signal classification
Nursing system
Sampling methods
Speech enhancement
Wheeze
Zero crossing and correlation
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Summary:In this paper, we will propose a method to detect an unusual voice for nursing system. Based on the healthy condition of a person, we define four kinds of unusual voices including cough, groan, wheeze and cry for help. When the person nursed sends out the unusual voices, we judge that his health condition have a doubt, and need someone to pay attention. In order to detect the unusual voices, we extract five features on audio waveform, including the number of segmented parts, duration of waveform, mean of volume, zero crossing rate and correlation. Experimental results show that the detection rate is 94%~97% for these four kinds of unusual voices. In false alarm, there are only 0.08% of wrong rates.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212146