A REAL-TIME UNUSUAL VOICE DETECTOR BASED ON NURSING AT HOME

Min-Quan Jing, Chao-Chun Wang, Ling-Hwei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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%similar to 97% for these four kinds of unusual voices. In false alarm, there are only 0.08% of wrong rates.
Original languageEnglish
Title of host publicationPROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6
PublisherIEEE
Pages2368-2373
Number of pages6
ISBN (Print)978-1-4244-4705-3
DOIs
StatePublished - 2009
Event2009 International Conference on Machine Learning and Cybernetics - Baoding, China
Duration: 12 Jul 200915 Jul 2009

Conference

Conference2009 International Conference on Machine Learning and Cybernetics
CountryChina
CityBaoding
Period12/07/0915/07/09

Keywords

  • Nursing system; Cough; groan; Wheeze; Cry for help; Zero crossing and correlation

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    Jing, M-Q., Wang, C-C., & Chen, L-H. (2009). A REAL-TIME UNUSUAL VOICE DETECTOR BASED ON NURSING AT HOME. In PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 (pp. 2368-2373). IEEE. https://doi.org/10.1109/ICMLC.2009.5212146