Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-28T22:31:28.015Z Has data issue: false hasContentIssue false
  • English
  • Français

Automated recording of stress vocalisations as a tool to document impaired welfare in pigs

Published online by Cambridge University Press:  11 January 2023

PC Schön ,
B Puppe  and
G Manteuffel
PC Schön
Affiliation:
Research Institute for the Biology of Farm Animals (FBN), Behavioural Physiology Unit, Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany
B Puppe
Affiliation:
Research Institute for the Biology of Farm Animals (FBN), Behavioural Physiology Unit, Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany
G Manteuffel*
Affiliation:
Research Institute for the Biology of Farm Animals (FBN), Behavioural Physiology Unit, Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany
*
* Contact for correspondence and requests for reprints: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The vocalisations of animals are results of particular emotional states. For example, the stress screams of pigs may be indicators of disturbed welfare. Our objective was to develop a system to monitor and record levels of stress calls in pigs, which could be employed in environments of breeding, transportation and slaughter. Using a combination of sound analysis by linear prediction coding and artificial neural networks, it was possible to detect the stress vocalisations of pigs in noisy pig units with few recognition errors (<5%). The system (STREMODO: stress monitor and documentation unit) running on PCs is insensitive to environmental noise, human speech and pig vocalisations other than screams. As a stand-alone device it can be routinely used for the objective, non-invasive measurement of acute stress in various farming environments. The system delivers reliable, reproducible registrations of stress vocalisations. Its detection quality in commercial systems was found to correlate well with that of human experts. STREMODO is particularly well-suited for comparisons of housing and management regimes. Since the system can be trained to recognise various animal vocalisations, its use with other species is also well within its scope.

Keywords

animal welfareneural networkspigstressvocalisationwelfare assessment
Type
Research Article
Information
Animal Welfare , Volume 13 , Issue 2 , May 2004 , pp. 105 - 110
Copyright
© 2004 Universities Federation for Animal Welfare

References

Blokhuis, H J, Hopster, H, Geverink, N A, Korte, S M and van Reenen, C G 1998 Studies of stress in farm animals. Comparative Haematology International 8: 94–101CrossRefGoogle Scholar
Broom, D M 1991 Animal welfare: concepts and measurement. Journal of Animal Science 69: 4167–4175CrossRefGoogle ScholarPubMed
Clark, J D, Rager, D R and Calpin, J D 1997 Animal well-being: 1. General considerations. Laboratory Animal Science 47: 564–570Google Scholar
Dreyfus, G 1992 Neural networks for the automatic recognition of handwritten digits. In: Schuster, H G (ed) Applications of Neural Networks pp 35–60. Verlag Chemie: Weinheim, GermanyGoogle Scholar
Fant, G 1970 Acoustic Theory of Speech Production. Mouton: The Hague, The NetherlandsGoogle Scholar
Gramß, T and Strube, H W 1992 Word recognition with a fast learning neural net. In: Schuster, H G (ed) Applications of Neural Networks pp 223–237. Verlag Chemie: Weinheim, GermanyGoogle Scholar
Jürgens, U 1979 Vocalisation as an emotional indicator. A neuroethological study in the squirrel monkey. Behaviour 69: 88–117CrossRefGoogle ScholarPubMed
Kanitz, E, Manteuffel, G and Otten, W 1998 Effects of weaning and restraint stress on glucocorticoid receptor binding capacity in limbic areas of domestic pigs. Brain Research 804: 311–315CrossRefGoogle ScholarPubMed
Kanitz, E, Otten, W, Nürnberg, G and Brüssow, K P 1999 Effects of age and maternal reactivity on the stress response of the pituitary-adrenocortical axis and the sympathetic nervous system in neonatal pigs. Animal Science 68: 519–526CrossRefGoogle Scholar
Manteuffel, G 2002 Central nervous regulation of the hypothalamic-pituitary-adrenal axis and its impact on fertility, immunity, metabolism and animal welfare — a review. Archiv für Tierzucht Dummerstorf 45: 575–595Google Scholar
Manteuffel, G and Puppe, B 2000 Animal well-being in husbandry and coping with stress. Archiv für Tierzucht Dummerstorf 43: 140–143 (Special issue)Google Scholar
Moshou, D, Chedad, A, Van Hirtum, A, De Baerdemaeker, J, Berkmans, D and Ramon, H 2001 Neural recognition system for swine cough. Mathematics and Computers in Simulation 56: 475–487CrossRefGoogle Scholar
Otten, W, Kanitz, E, Tuchscherer, M and Nürnberg, G 2001 Effects of prenatal restraint stress on hypothalamic-pituitary-adrenocortical and sympatho-adrenomedullary axis in neonatal pigs. Animal Science 73: 279–287Google Scholar
Pham, D T and Le Breton, A 1991 Levinson-Durbin type algorithm for continuous-time autoregressive models and applications. Mathematics Control Signals Systems 4: 69–79CrossRefGoogle Scholar
Rosenblatt, F 1962 Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books: Washington, DC, USACrossRefGoogle Scholar
Schön, P C and Manteuffel, G 2001 Nichtinvasive Beurteilung emotionaler Stressbelastung von Nutztieren mittels Vokalisationserkennung durch ein künstliches neuronales Netzwerk. In: Schäfer, D and von Borell, E (eds) Proceedings of the 15th IGN Congress: Tierschutz und Nutztierhaltung (Animal Welfare and Farm Animal Housing) pp 104–109. Internationale Gesellschaft für Nutztierhaltung: Halle, Germany [Title translation: Noninvasive judgement of emotional stress of farm animals by vocalisation recognition using an artificial neuronal network]: Noninvasive judgement of emotional stress of farm animals by vocalisation recognition using an artificial neuronal network]Google Scholar
Schön, P C, Puppe, B and Manteuffel, G 1998 A sound analysis system based on LabVIEW® applied to the analysis of suckling grunts of domestic pigs (Sus scrofa). Bioacoustics 9: 119–133CrossRefGoogle Scholar
Schön, P C, Puppe, B and Manteuffel, G 1999 Common features and individual differences in nurse grunting of domestic pigs (Sus scrofa): a multi-parametric analysis. Behaviour 136: 49–66CrossRefGoogle Scholar
Schön, P C, Puppe, B and Manteuffel, G 2001 Linear prediction coding analysis and self-organizing feature map as tools to classify stress calls of domestic pigs (Sus scrofa). Journal of the Acoustical Society of America 110: 1425–1431CrossRefGoogle ScholarPubMed
Schräder, L and Rohn, C 1997 Lautäußerungen von Hausschweinen als Indikator für Stressreaktionen. Landbauforschung Völkenrode 47: 89–95 [Title translation: The quality of pig vocalisations as an indicator of stress response]Google Scholar
Schräder, L and Todt, D 1998 Vocal quality is correlated with levels of stress hormones in domestic pigs. Ethology 104: 859–876CrossRefGoogle Scholar
Tuchscherer, M, Kanitz, E, Otten, W and Tuchscherer, A 2002 Effects of prenatal stress on cellular and humoral immune responses in neonatal pigs. Veterinary Immunology and Immunopathology 86: 175–203CrossRefGoogle ScholarPubMed
Weary, D M and Fraser, D 1995 Calling by domestic piglets: reliable signals of need? Animal Behaviour 50: 1047–1055CrossRefGoogle Scholar