Cough sound analysis to identify respiratory infection in pigs

Ferrari, Sara; Silva, Mitchell; Guarino, Marcella; Aerts, Jean Marie; Berckmans, Daniël

doi:10.1016/j.compag.2008.07.003

Cited by 81 publications

(58 citation statements)

References 29 publications

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“…This finding was later supported by Ferrari et al (2008) who studied the acoustic differences in cough sounds from the infected lungs of pigs compared to the cough of healthy pigs, induced by inhalation of citric acid. The healthy pigs were found to have a significantly higher peak frequency and shorter duration of cough compared to the infected pigs.…”

Section: Past Research On Coughsupporting

confidence: 50%

Measuring Voluntary Cough and Its Relationship to the Perception of Voice

Zawawi¹,

Robb²,

O’Beirne³

2012

Asia Pacific Journal of Speech, Language and Hearing

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Section: Past Research On Coughsupporting

confidence: 50%

Measuring Voluntary Cough and Its Relationship to the Perception of Voice

Zawawi¹,

Robb²,

O’Beirne³

2012

Asia Pacific Journal of Speech, Language and Hearing

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“…116 Labelling is a manual procedure based on acoustic analysis combined with visual spectral analysis, which is 124 used to extract fragments of sounds from the entire recording. The labelling procedure was done offline by 125 extrapolating those sounds that the operator classified as significant vocalisation sounds via auditive 126 analysis and visual observation of the spectrogram (Ferrari et al, 2008). 127 6 Through Adobe® Audition TM CS6 each sounds were identified and analysed using time (x-axis) and 128 frequency (y-axis).…”

mentioning

confidence: 99%

An innovative approach to predict the growth in intensive poultry farming

Fontana

Tullo

Butterworth

et al. 2015

Computers and Electronics in Agriculture

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Abstract 10Chicken weight provides information about growth and feed conversion of the flock in order to identify 11 deviations from the expected homogeneous growth trend of the birds. This paper proposes a novel method 12 to automatically measure the growth rate of broiler chickens by sound analysis. 13Through the application of process engineering, Precision Livestock Farming (PLF) can combine audio and 14 video information into on-line automated tools that can be used to control, monitor and model the 15 behaviour, health and production of animals and their biological response. 16The aim of this study was to record and analyse broiler vocalisations under normal farm conditions, to 17 identify the relation between animal sounds and growth trend. Recordings were made at regular intervals, 18 during the entire life of birds, in order to evaluate the variation of frequency and bandwidth of the sounds 19 emitted by the animals. 20Two experimental trials were carried out in an indoor reared broiler farm; the audio recording procedures 21 lasted for 38 days. The recordings were made, in an automated, non-invasive and non-intrusive way and 22 without disturbing the animals in to the broiler house. Once a week, 50 birds were selected at random and 23 their weight recorded in order to follow the growth trend in the birds. 24Sound recordings were manually analysed and labelled using the Adobe Audition CS6 software. 25 2 Analysing the sounds recorded, it was possible to find a significant correlation (P<0.001) between the 26 frequencies of the vocalisations recorded and the weight of the broilers. 27The results explained how the frequency of the sounds emitted by the animals was inversely proportional 28 to the age and to the weight of the broilers; the more they grow, the lower the frequency of the sounds 29 emitted by the animals. 30This preliminary study shows how this method based on the identification of specific frequencies of the 31 sounds, in an indoor reared broiler farm, linked to the age and to the weight of the birds, could be used as 32 an early warning method/system to evaluate the health and welfare status of the animals at farm level, 33 developing also an automated growth monitoring tool. 34

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“…The labelling procedure was carried out offline, identifying those sounds that the operator classified as significant vocalisation sounds through the combination of auditive analysis (listening with headphones) and the visual observation of the spectrogram of the sounds corresponding to each sound 'group' (Ferrari et al, 2008). Through Adobe ® Audition TM CS6, a number of discrete sound 'groups' were identified and analysed using time (x-axis) and frequency (y-axis) for further statistical comparisons.…”

Section: Methodsmentioning

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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Cough sound analysis to identify respiratory infection in pigs

Cited by 81 publications

References 29 publications

Measuring Voluntary Cough and Its Relationship to the Perception of Voice

Measuring Voluntary Cough and Its Relationship to the Perception of Voice

An innovative approach to predict the growth in intensive poultry farming

Vocalisation sound pattern identification in young broiler chickens

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