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

Research output: Contribution to journal › Journal article › Research › peer-review

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Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production. / Briefer, Elodie F.; Sypherd, Ciara C.-R.; Linhart, Pavel; Leliveld, Lisette M. C.; de la Torre, Monica Padilla; 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.

In: Scientific Reports, Vol. 12, 3409, 2022.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Briefer, EF, Sypherd, CC-R, Linhart, P, Leliveld, LMC, de la Torre, MP, Read, ER, Guérin, C, Deiss, V, Monestier, C, Rasmussen, JH, Špinka, M, Düpjan, S, Boissy, A, Janczak, AM, Hillmann, E & Tallet, C 2022, 'Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production', Scientific Reports, vol. 12, 3409. https://doi.org/10.1038/s41598-022-07174-8

APA

Briefer, E. F., Sypherd, C. C-R., Linhart, P., Leliveld, L. M. C., de la Torre, M. P., Read, E. R., Guérin, C., Deiss, V., Monestier, C., Rasmussen, J. H., Špinka, M., Düpjan, S., Boissy, A., Janczak, A. M., Hillmann, E., & Tallet, C. (2022). Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production. Scientific Reports, 12, [3409]. https://doi.org/10.1038/s41598-022-07174-8

Vancouver

Briefer EF, Sypherd CC-R, Linhart P, Leliveld LMC, de la Torre MP, Read ER et al. Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production. Scientific Reports. 2022;12. 3409. https://doi.org/10.1038/s41598-022-07174-8

Author

Briefer, Elodie F. ; Sypherd, Ciara C.-R. ; Linhart, Pavel ; Leliveld, Lisette M. C. ; de la Torre, Monica Padilla ; 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. / Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production. In: Scientific Reports. 2022 ; Vol. 12.

Bibtex

@article{3476bab4b49442acbb8d9fdcd57881a3,

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

abstract = "Vocal 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.",

author = "Briefer, {Elodie F.} and Sypherd, {Ciara C.-R.} and Pavel Linhart and Leliveld, {Lisette M. C.} and {de la Torre}, {Monica Padilla} and Read, {Eva R.} and Carole Gu{\'e}rin and V{\'e}ronique Deiss and Chlo{\'e} Monestier and Rasmussen, {Jeppe H.} and Marek {\v S}pinka and Sandra D{\"u}pjan and Alain Boissy and Janczak, {Andrew M.} and Edna Hillmann and C{\'e}line Tallet",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

doi = "10.1038/s41598-022-07174-8",

language = "English",

volume = "12",

journal = "Scientific Reports",

issn = "2045-2322",

publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

AU - Briefer, Elodie F.

AU - Sypherd, Ciara C.-R.

AU - Linhart, Pavel

AU - Leliveld, Lisette M. C.

AU - de la Torre, Monica Padilla

AU - Read, Eva R.

AU - Guérin, Carole

AU - Deiss, Véronique

AU - Monestier, Chloé

AU - Rasmussen, Jeppe H.

AU - Špinka, Marek

AU - Düpjan, Sandra

AU - Boissy, Alain

AU - Janczak, Andrew M.

AU - Hillmann, Edna

AU - Tallet, Céline

PY - 2022

Y1 - 2022

N2 - Vocal 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.

AB - Vocal 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.

U2 - 10.1038/s41598-022-07174-8

DO - 10.1038/s41598-022-07174-8

M3 - Journal article

C2 - 35256620

AN - SCOPUS:85126079708

VL - 12

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 3409

ER -

ID: 302899677

Department of Biology