ArticleScientific reports2022
Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 30 papers.
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30 citing papers in PubMed, 97 citations in OpenAlex.
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- TweetyBERT: Automated parsing of birdsong through self-supervised machine learning.Patterns (New York, N.Y.) · 2026Article
- Developing and Assessing the Performance of a Machine Learning Model for Analyzing Drinking Behaviors in Minipigs for Experimental Research.Sensors (Basel, Switzerland) · 2026Article
- Investigating context-specific acoustic variation in the bleats of male domestic sheep.Open life sciences · 2026Article
- Human recognition of emotional valence and arousal of zoo animals.Scientific reports · 2025Article
- Bridging human and animal personality: A behavioral test to assess reward sensitivity.iScience · 2025Article
- Framework for Classification of Fattening Pig Vocalizations in a Conventional Farm with High Relevance for Practical Application.Animals : an open access journal from MDPI · 2025Article
- Non-Invasive Human-Free Diagnosis Methods for Assessing Pig Welfare at Abattoirs: A Systematic Review.Animals : an open access journal from MDPI · 2025Review
- Companion pigs alternate orientation between humans and snout-inaccessible targets.Royal Society open science · 2025Article
- Review: Gut Microbiota-A Powerful Tool for Improving Pig Welfare by Influencing Behavior Through the Gut-Brain Axis.Animals : an open access journal from MDPI · 2025Review
- Dynamic Multi-Behaviour, Orientation-Invariant Re-Identification of Holstein-Friesian Cattle.Sensors (Basel, Switzerland) · 2025Article
- Machine learning algorithms can predict emotional valence across ungulate vocalizations.iScience · 2025Article
- Blood Sampling in Göttingen Minipigs-A Case Study of Two Standard Methods and Clicker Training as a Restraint-Free Alternative.Animals : an open access journal from MDPI · 2025Article
- Integrating multi-modal data fusion approaches for analysis of dairy cattle vocalizations.Frontiers in veterinary science · 2025Article
- Affective Implications of Human-Animal Relationship on Pig Welfare: Integrating Non-Linear Heart Rate Variability Measures.Animals : an open access journal from MDPI · 2024Article
- Domestication and exposure to human social stimuli are not sufficient to trigger attachment to humans: a companion pig-dog comparative study.Scientific reports · 2024Article
- Facial representations of complex affective states combining pain and a negative emotion.Scientific reports · 2024Article
- Detecting tail biters by monitoring pig screams in weaning pigs.Scientific reports · 2024Article
- Human-computer interactions with farm animals-enhancing welfare through precision livestock farming and artificial intelligence.Frontiers in veterinary science · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
16 authors at 10 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.