Stress Detection and Classification of Laying Hens by Sound Analysis

  • Lee, Jonguk (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Noh, Byeongjoon (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Jang, Suin (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Park, Daihee (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Chung, Yongwha (Department of Computer and Information Science, College of Science and Technology, Korea University) ;
  • Chang, Hong-Hee (Department of Animal Science, Institute of Agriculture and Life Sciences, College of Agriculture and Life Sciences, Gyeongsang National University)
  • Received : 2014.08.26
  • Accepted : 2014.12.15
  • Published : 2015.04.01


Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.


Laying Hens;Stress Recognition;Sound Analysis;Monitoring System


Grant : 과학벨트 연계형 융합 SW 고급 인재 양성 사업팀


  1. Balnave, D. and S. Muheereza. 1997. Improving eggshell quality at high temperatures with dietary sodium bicarbonate. Poult. Sci. 76:588-593.
  2. Blahova, J., R. Dobsikova, E. Strakova, and P. Suchy. 2007. Effect of low environmental temperature on performance and blood system in broiler chickens (Gallus domesticus). Acta Vet. Brno 76:17-23.
  3. Boersma, P. 2002. Praat, a system for doing phonetics by computer. Glot Int. 5:341-345.
  4. Chung, Y., S. Oh, J. Lee, D. Park, H. H. Chang, and S. Kim. 2013a. Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors 13:12929-12942.
  5. Chung, Y., J. Lee, S. Oh, D. Park, H.H. Chang, and S. Kim. 2013b. Automatic detection of cow's oestrus in audio surveillance system. Asian Australas. J. Anim. Sci. 26:1030-1037.
  6. Cristianini, N. and J. Shawe-Taylor. 2000. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge, England.
  7. Elrom, K. 2000. Handling and transportation of broilers; welfare, stress, fear and meat quality. Part III: Fear; definitions, its relation to stress, causes of fear, responses of fear and measurement of fear. Israel J. Vet. Med. 55:1-3.
  8. Freeman, B. M. 1976. Stress and the domestic fowl: a physiological re-appraisal. World's Poult. Sci. J. 32:249-256.
  9. George, J. K. and Y. Bo. 2008. Fuzzy Sets and Fuzzy Logic, Theory and Applications. Prentice-Hall, Upper Saddle River, NJ, USA.
  10. Gutierrez, W. M. 2013. Effects of Heat stress on the Productivity of Pigs and Laying Hens. Ph. D. Thesis, Gyeongsang National University in Korea, Jinju, Korea.
  11. Guyer, P. 2009. Engineering SoundBite: Fundamentals of Acoustics. Guyer Partners, Accessed Jun 10, 2014.
  12. Hall, M. A. 1999. Correlation-based Feature Selection for Machine Learning. Ph. D. Thesis, University of Waikato in New Zealand, Waikato, New Zealand.
  13. Han, J., M. Kamber, and J. Pei. 2012. Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, Burlington, MA, USA.
  14. Harvey, S., J. G. Phillips, A. Rees, and T. R. Hall. 1984. Stress and adrenal function. J. Exp. Zool. 232:633-645.
  15. Lee, J., S. Zuo, Y. Chung, D. Park, H. H. Chang, and S. Kim. 2014. Formant-based acoustic features for cow's estrus detection in audio surveillance system. In Advanced Video and Signal Based Surveillance (AVSS), 2014 11th IEEE International Conference on IEEE, Seoul, Korea. 236-240.
  16. Lee, J.-Y., J.-H. Lee, J.-S. Yeo, and J.-J. Kim. 2013. A SNP harvester analysis to better detect SNPs of CCDC158 gene that are associated with carcass quality traits in Hanwoo. Asian Australas. J. Anim. Sci. 26:766-771.
  17. Mahmoud, K. Z., M. M. Beck, S. E. Scheideler, M. F. Forman, K. P. Anderson, and S. D. Kachman. 1996. Acute high environmental temperature and calcium-estrogen relationships in the hen. Poult. Sci. 75:1555-1562.
  18. Muiruri, H. K. and P. C. Harrison. 1991. Effect of peripheral foot cooling on metabolic rate and thermoreregulation of fed and fasted chicken hens in a hot environment. Poult. Sci. 70:74-79.
  19. Mushtaq, M. M. H., T. N. Pasha, Saima, M. Akram, T. Mushtaq, R. Parvin, U. Farooq, S. Mehmood, K. J. Iqbal, and J. Hwangbo. 2013. Growth performance, carcass traits and serum mineral chemistry as affected by dietary sodium and sodium salts fed to broiler chickens reared under phase feeding system. Asian Australas. J. Anim. Sci. 26:1742-1752.
  20. Otu-Nyarko, E. 2010. The Effect of Stress on the Vocalizations of Captive Poultry Populations. Ph. D. Thesis, University of Connecticut, Storrs, CT, USA.
  21. Schrader, L. and K. Hammerschmidt. 1997. Computer-aided analysis of acoustic parameters in animal vocalizations: A multi-parametric approach. Bioacoustics 7:247-265.
  22. Slocombe, K. E. and K. Zuberbuhler. 2006. Food-associated calls in chimpanzees: responses to food types or food preferences? Anim. Behav. 72:989-999.
  23. Steen, K. A., O. R. Therkildsen, H. Karstoft, and O. Green. 2012. A vocal-based analytical method for goose behaviour recognition. Sensors 12:3773-3788.
  24. Tefera, M. 2012. Acoustic Signals in Domestic Chicken (Gallus gallus): A Tool for teaching veterinary ethology and implication for language learning. Ethiop. Vet. J. 16:77-84.
  25. Waldvogel, J. A. 2000. Birdsong playback as a tool for teaching animal behavior. Tested Studies for Laboratory Teaching 22:247-260.
  26. Yu, J., H. Lee, Y. Im, M.-S. Kim, and D. Park. 2010. Real-time classification of Internet application traffic using a hierarchical multi-class SVM. KSII Transactions on Internet and Information Systems (TIIS) 4:859-876.

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