Automated identification of chicken distress vocalisations using deep learning models

Mao, Axiu; Giraudet, Claire S E; Liu, Kai; Nolasco, Ines; ZhiQin, Xie; ZhiXun, Xie; Gao, Yue; Theobald, James; Bhatta, Devaki; Stewart, Rebecca; McElligott, Alan G.

doi:10.1101/2021.12.15.472774

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…Technological advancements have allowed for an increase in automated techniques that have made these systems more maintainable. For example, Mao and colleagues [ 97 ] reported a 55.88% faster detection speed when using automated monitoring systems for chicken distress calls compared to manual observations. Extensive literature for multiple species exists on the association of vocal behavior and maladaptive behaviors associated with distress, stress, aversion, aggression, and stereotypy [ 91 , 93 , 98 , 99 , 100 ], but the utilization of vocal biomarkers for welfare monitoring has, to our knowledge, yet to be implemented for animals in professional care outside of livestock contexts.…”

Section: Acoustic Behavior Monitoringmentioning

confidence: 99%

Acoustic Monitoring of Professionally Managed Marine Mammals for Health and Welfare Insights

Winship

Jones

2023

Animals

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Research evaluating marine mammal welfare and opportunities for advancements in the care of species housed in a professional facility have rapidly increased in the past decade. While topics, such as comfortable housing, adequate social opportunities, stimulating enrichment, and a high standard of medical care, have continued to receive attention from managers and scientists, there is a lack of established acoustic consideration for monitoring the welfare of these animals. Marine mammals rely on sound production and reception for navigation and communication. Regulations governing anthropogenic sound production in our oceans have been put in place by many countries around the world, largely based on the results of research with managed and trained animals, due to the potential negative impacts that unrestricted noise can have on marine mammals. However, there has not been an established best practice for the acoustic welfare monitoring of marine mammals in professional care. By monitoring animal hearing and vocal behavior, a more holistic view of animal welfare can be achieved through the early detection of anthropogenic sound sources, the acoustic behavior of the animals, and even the features of the calls. In this review, the practice of monitoring cetacean acoustic welfare through behavioral hearing tests and auditory evoked potentials (AEPs), passive acoustic monitoring, such as the Welfare Acoustic Monitoring System (WAMS), as well as ideas for using advanced technologies for utilizing vocal biomarkers of health are introduced and reviewed as opportunities for integration into marine mammal welfare plans.

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Section: Acoustic Behavior Monitoringmentioning

confidence: 99%

Acoustic Monitoring of Professionally Managed Marine Mammals for Health and Welfare Insights

Winship

Jones

2023

Animals

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“…An example of distress calls (10 s duration) is provided as electronic supplementary material [62]. Data for the analyses reported can be accessed via the Figshare Digital Repository: https://doi.org/10.6084/m9.figshare.20049722.…”

Section: Data Accessibilitymentioning

confidence: 99%

Automated identification of chicken distress vocalizations using deep learning models

Mao

Giraudet

Liu

et al. 2022

J. R. Soc. Interface.

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The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an ‘iceberg indicator’ of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.

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Trend and Representativeness of Acoustic Features of Broiler Chicken Vocalisations Related to CO2

Ginovart-Panisello

Iriondo²,

Monjo³

et al. 2022

Applied Sciences

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The concentration of CO2 is relatively large in poultry farms and high accumulations of this gas reduce animal welfare. Good control of its concentration is crucial for the health of the animals. The vocalizations of the chickens can show their level of well-being linked to the presence of carbon dioxide. An audio recording system was implemented and audio raw data was processed to extract acoustical features from four cycles of forty days, three of them from the same farm. This research aims to find the most relevant acoustic features extracted from the broiler’s calls that are related to the CO2 concentration and that could help to automate procedures. The results are encouraging since MFCC 6, 9, 4 and 3 are the most important features that relate the vocalizations of the chickens to the gas concentration, furthermore there is a clear and more similar representativeness trend during birds’ life period from day 15 to day 40.

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Automated identification of chicken distress vocalisations using deep learning models

Cited by 3 publications

References 57 publications

Acoustic Monitoring of Professionally Managed Marine Mammals for Health and Welfare Insights

Acoustic Monitoring of Professionally Managed Marine Mammals for Health and Welfare Insights

Automated identification of chicken distress vocalizations using deep learning models

Trend and Representativeness of Acoustic Features of Broiler Chicken Vocalisations Related to CO2

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