Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Author:

Briefer Elodie F.,Sypherd Ciara C. -R.,Linhart Pavel,Leliveld Lisette M. C.,Padilla de la Torre Monica,Read Eva R.,Guérin Carole,Deiss Véronique,Monestier Chloé,Rasmussen Jeppe H.,Špinka Marek,Düpjan Sandra,Boissy Alain,Janczak Andrew M.,Hillmann Edna,Tallet Céline

Abstract

AbstractVocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.

Funder

Bundesamt für Lebensmittelsicherheit und Veterinärwesen

Ministerstvo Zemědělství

Bundesanstalt für Landwirtschaft und Ernährung

Mattilsynet

Agence Nationale de la Recherche

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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