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Time-series analysis for online recognition and localization of sick pig (sus scrofa) cough sounds

Exadaktylos, Vasileios and Silva, Mitchell and Ferrari, Sara and Guarino, Marcella and Taylor, C. James and Aerts, Jean-Marie and Berckmans, Daniel (2008) Time-series analysis for online recognition and localization of sick pig (sus scrofa) cough sounds. Journal of the Acoustical Society of America, 124 (6). pp. 3803-3809. ISSN 0001-4966

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Abstract

This paper considers the online localization of sick animals in pig houses. It presents an automated online recognition and localization procedure for sick pig cough sounds. The instantaneous energy of the signal is initially used to detect and extract individual sounds from a continuous recording and their duration is used as a pre–classifier. Auto–regression (AR) analysis is then employed to calculate an estimate of the sound signal and the parameters of the estimated signal are subsequently evaluated to identify the sick cough sounds. It is shown that the distribution of just 3 AR parameters provides an ade-quate classifier for sick pig coughs. A localization technique based on the time difference of arrival is evaluated on field data and is shown that it is of acceptable accuracy for this particular application. The algorithm is applied on continuous recordings from a pig house to evaluate its effectiveness. The correct identification ratio ranged from 73% (27% false positive identifications) to 93% (7% false positive identifications) depending on the position of the microphone that was used for the recording. Although the false negative identifications are about 50% it is shown that this accuracy can be enough for the purpose of this tool. Finally, it is suggested that the presented application can be used to online monitor the welfare in a pig house, and provide early diagnosis of a cough hazard and faster treatment of sick animals.

Item Type: Article
Journal or Publication Title: Journal of the Acoustical Society of America
Uncontrolled Keywords: acoustic signal detection ; acoustic signal processing ; autoregressive processes ; bioacoustics ; diseases ; pattern classification ; time series
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: Faculty of Science and Technology > Engineering
ID Code: 33437
Deposited By: Dr C. James Taylor (Engineering)
Deposited On: 18 May 2010 11:45
Refereed?: Yes
Published?: Published
Last Modified: 24 Jan 2014 05:18
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/33437

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