sound duration, amplitude, fundamental frequency, energy distribution) 7, 8.ĭue to the impact of emotions on vocalization, the analysis of vocal expression of emotions is increasingly being considered as an important non-invasive tool to assess the affective aspects of animal welfare 9, 10. ![]() During vocal production, emotions can influence the physiological structures that are the basis of sound production at several levels (lungs, larynx and vocal tract), thus modifying sound structure itself (e.g. The dimensional approach, that categorizes emotions according to their two main dimensions-their valence (pleasant/positive versus unpleasant/negative) and their arousal (bodily activation) -, offers a good framework to study emotional experiences in animals 4.Įmotions can be expressed through visual, olfactory, and vocal signals to allow the regulation of social interactions 5, 6. This line of thinking suggests that an emotion is triggered by the evaluation that an individual makes of its environmental situation 3. ![]() Research in animals confirms that emotions are not automatic and reflexive processes, but can rather be explained by elementary cognitive processes 2. These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.Īnimal emotions, defined as short-term intense affective reactions to specific events, have been of increasing interest over the last few decades, especially because of the growing concern for animal welfare 1. 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%). Similarly, the context category affected all parameters. Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. 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). 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. Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions.
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