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Real-time recognition of sick pig cough sounds

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Real-time recognition of sick pig cough sounds. / Exadaktylos, V.; Silva, M.; Aerts, J.-M.; Taylor, C. James; Berckmans, D.

In: Computers and Electronics in Agriculture, Vol. 63, No. 2, 10.2008, p. 207-214.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Exadaktylos, V, Silva, M, Aerts, J-M, Taylor, CJ & Berckmans, D 2008, 'Real-time recognition of sick pig cough sounds', Computers and Electronics in Agriculture, vol. 63, no. 2, pp. 207-214. https://doi.org/10.1016/j.compag.2008.02.010

APA

Exadaktylos, V., Silva, M., Aerts, J-M., Taylor, C. J., & Berckmans, D. (2008). Real-time recognition of sick pig cough sounds. Computers and Electronics in Agriculture, 63(2), 207-214. https://doi.org/10.1016/j.compag.2008.02.010

Vancouver

Exadaktylos V, Silva M, Aerts J-M, Taylor CJ, Berckmans D. Real-time recognition of sick pig cough sounds. Computers and Electronics in Agriculture. 2008 Oct;63(2):207-214. https://doi.org/10.1016/j.compag.2008.02.010

Author

Exadaktylos, V. ; Silva, M. ; Aerts, J.-M. ; Taylor, C. James ; Berckmans, D. / Real-time recognition of sick pig cough sounds. In: Computers and Electronics in Agriculture. 2008 ; Vol. 63, No. 2. pp. 207-214.

Bibtex

@article{3df1fb5479e042a2b70feb271fb59833,
title = "Real-time recognition of sick pig cough sounds",
abstract = "This paper extends existing cough identification methods and proposes a real-time method for identifying sick pig cough sounds. The analysis and classification is based on the frequency domain characteristics of the signal, while an improved procedure to extract the reference is presented. This technique evaluates fuzzy c-means clustering to parts of the training signals and provides a frequency content reference that mirrors the characteristics of sick pig cough. The extraction of the reference is performed in such a way that allows for the identification process to be implemented in real-time applications that would speed up the diagnosis and treatment process and improve animal welfare in pig houses. Preliminary results for the evaluation of the algorithm are based on individual sounds of healthy and sick animals acquired in laboratory conditions. An 85% overall correct classification ratio is achieved with 82% of the sick cough sounds being correctly identified.",
keywords = "Real-time recognition, Cough analysis, Pig vocalisations, Biomedical system, Spectral analysis",
author = "V. Exadaktylos and M. Silva and J.-M. Aerts and Taylor, {C. James} and D. Berckmans",
year = "2008",
month = oct,
doi = "10.1016/j.compag.2008.02.010",
language = "English",
volume = "63",
pages = "207--214",
journal = "Computers and Electronics in Agriculture",
issn = "0168-1699",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - Real-time recognition of sick pig cough sounds

AU - Exadaktylos, V.

AU - Silva, M.

AU - Aerts, J.-M.

AU - Taylor, C. James

AU - Berckmans, D.

PY - 2008/10

Y1 - 2008/10

N2 - This paper extends existing cough identification methods and proposes a real-time method for identifying sick pig cough sounds. The analysis and classification is based on the frequency domain characteristics of the signal, while an improved procedure to extract the reference is presented. This technique evaluates fuzzy c-means clustering to parts of the training signals and provides a frequency content reference that mirrors the characteristics of sick pig cough. The extraction of the reference is performed in such a way that allows for the identification process to be implemented in real-time applications that would speed up the diagnosis and treatment process and improve animal welfare in pig houses. Preliminary results for the evaluation of the algorithm are based on individual sounds of healthy and sick animals acquired in laboratory conditions. An 85% overall correct classification ratio is achieved with 82% of the sick cough sounds being correctly identified.

AB - This paper extends existing cough identification methods and proposes a real-time method for identifying sick pig cough sounds. The analysis and classification is based on the frequency domain characteristics of the signal, while an improved procedure to extract the reference is presented. This technique evaluates fuzzy c-means clustering to parts of the training signals and provides a frequency content reference that mirrors the characteristics of sick pig cough. The extraction of the reference is performed in such a way that allows for the identification process to be implemented in real-time applications that would speed up the diagnosis and treatment process and improve animal welfare in pig houses. Preliminary results for the evaluation of the algorithm are based on individual sounds of healthy and sick animals acquired in laboratory conditions. An 85% overall correct classification ratio is achieved with 82% of the sick cough sounds being correctly identified.

KW - Real-time recognition

KW - Cough analysis

KW - Pig vocalisations

KW - Biomedical system

KW - Spectral analysis

U2 - 10.1016/j.compag.2008.02.010

DO - 10.1016/j.compag.2008.02.010

M3 - Journal article

VL - 63

SP - 207

EP - 214

JO - Computers and Electronics in Agriculture

JF - Computers and Electronics in Agriculture

SN - 0168-1699

IS - 2

ER -