Discerning Pig Screams in Production Environments

Vandermeulen, Joris; Bahr, Claudia; Tullo, Emanuela; Fontana, Ilaria; Ott, Sanne; Kashiha, Mohammad Amin; Moons, Christel; Tuyttens, Frank; Niewold, Theodoor; Berckmans, Daniël

doi:10.1371/journal.pone.0123111

Cited by 51 publications

(32 citation statements)

References 38 publications

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“…Other approaches of PLF specifically with sound analysis worth mentioning are for instance, the detection and counting of screams in pigs for stress assessment (Schön et al, 2004;Moura et al, 2008a;Vandermeulen et al, 2015). Acoustic monitoring was also used to estimate the dry matter intake of grazing sheep (Galli et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds

Vandermeulen

Bahr

Johnston

et al. 2016

Computers and Electronics in Agriculture

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a b s t r a c tBovine respiratory disease (BRD) complex in calves impairs health and welfare and causes severe economic losses for the Stockperson. Early recognition of BRD should lead to earlier veterinary (antibiotic/ anti-inflammatory) treatment interventions thereby reducing the severity of the disease and associated costs. Coughing is one of the clinical manifestations of BRD. It is believed that by automatically and continuously monitoring the sounds within calf houses, and analysing the coughing frequency, early recognition of BRD in calves is possible. Therefore, the objective of the present study was to develop an automated calf cough monitor and examine its potential as an early warning system for BRD in artificially reared dairy calves. The coughing sounds of 62 calves were continuously recorded by a microphone over a three-month period. A sound analysis algorithm was developed to distinguish calf coughs from other sounds (e.g. mechanical sounds). During the sound recording period the health of the calves was assessed and scored periodically per week by a trained human observer. Calves presenting with BRD received antibiotic and/or anti-inflammatory treatment and the dates of treatment were recorded. This treatment date reference served as a comparison for the investigation of whether an increase in coughing frequency could be related to calves developing BRD. The calf cough detection algorithm achieved 50.3% sensitivity, 99.2% specificity and 87.5% precision. Four out of five periods, where coughing frequency was observed to be increased, coincided with the development of BRD in more than one calf. This period of increased coughing frequency was always observed before the calves were treated. Therefore, the calf cough monitor has the potential to identify early onset of BRD in calves.

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Section: Introductionmentioning

confidence: 99%

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds

Vandermeulen

Bahr

Johnston

et al. 2016

Computers and Electronics in Agriculture

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“…Social isolation, castration, and weaning were all procedures that resulted in high call rates, with high frequency, duration, and amplitude (Manteuffel et al., 2004). Additionally, feed deprivation can be detected by analysing the number of screams (Vandermeulen et al., 2015), and the stressful procedure of handling piglets also results in changes in vocalisations (Moura et al., 2008). …”

Section: Behavioural Categoriesmentioning

confidence: 99%

Early detection of health and welfare compromises through automated detection of behavioural changes in pigs

Matthews

Miller

Clapp

et al. 2016

The Veterinary Journal

205

129

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HighlightsRecent developments in automatically detecting compromised pig health and welfare.Five categories of behaviour are reviewed.Behaviours mapped to sensors that are feasible for automated detection.Progress towards levels of automation through detection, monitoring and fully automatic detection of behavioural change.Challenges for automated detection of behavioural changes are multifaceted and require trade-offs to develop such systems.

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“…Assessment of behaviours may provide indirect evidence of how an animal 'feels' (Dawkins, 2004). Animals use vocalisations to express different conditions such as: warning, alarm, social contact, territorial, laying, nesting, mating, threat, submissive, distress, fear, contentment, food, dust bathing, perching, battle cries, privacy, dominance and time calls (Manteuffel et al, 2004;Tefera, 2012;Vandermeulen et al, 2015). One of the potential ways to assess an animal's health and welfare status is the analysis of audio and video to identify behaviours, vocal and other sound producing behaviours.…”

Section: Introductionmentioning

confidence: 99%

Vocalisation sound pattern identification in young broiler chickens

Fontana

Tullo

Scrase

et al. 2016

animal

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In this study, we describe the monitoring of young broiler chicken vocalisation, with sound recorded and assessed at regular intervals throughout the life of the birds from day 1 to day 38, with a focus on the first week of life. We assess whether there are recognisable, and even predictable, vocalisation patterns based on frequency and sound spectrum analysis, which can be observed in birds at different ages and stages of growth within the relatively short life of the birds in commercial broiler production cycles. The experimental trials were carried out in a farm where the broiler where reared indoor, and audio recording procedures carried out over 38 days. The recordings were made using two microphones connected to a digital recorder, and the sonic data was collected in situations without disturbance of the animals beyond that created by the routine activities of the farmer. Digital files of 1 h duration were cut into short files of 10 min duration, and these sound recordings were analysed and labelled using audio analysis software. Analysis of these short sound files showed that the key vocalisation frequency and patterns changed in relation to increasing age and the weight of the broilers. Statistical analysis showed a significant correlation ( P < 0.001) between the frequency of vocalisation and the age of the birds. Based on the identification of specific frequencies of the sounds emitted, in relation to age and weight, it is proposed that there is potential for audio monitoring and comparison with 'anticipated' sound patterns to be used to evaluate the status of farmed broiler chicken.

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Discerning Pig Screams in Production Environments

Cited by 51 publications

References 38 publications

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds

Early detection of health and welfare compromises through automated detection of behavioural changes in pigs

Vocalisation sound pattern identification in young broiler chickens

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