Automatic detection of cow/calf vocalizations in free-stall barn

Ntalampiras, Stavros; Pezzuolo, Andrea; Mattiello, S.; Battini, M.; Brščić, Marta

doi:10.1109/tsp49548.2020.9163522

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Vocalizations are often involuntary, especially those indicating negative affective states, and so are good indicators of immediate emotional reactions in animals [9]. ML approaches to farm animal sound analyses have been extensively explored and successfully applied to analysing vocalizations from pigs [76], chickens [75,77] and cows [78,79].…”

Section: Microphones-vocalisationsmentioning

confidence: 99%

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Neethirajan¹

2022

Animals

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Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

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Section: Microphones-vocalisationsmentioning

confidence: 99%

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Neethirajan¹

2022

Animals

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“…This confirms that for behaviors with a low frequency and short duration, like vocalizations, only CS is a suitable option. Regardless of the sampling method and way to deal with out of sight, behavioral observations seem not being enough to effectively evaluate vocalizations (type and objective of them), and other adequate instruments and more specific tools are required for this purpose ( 50 , 51 ).…”

Section: Discussionmentioning

confidence: 99%

A methodological approach to compare continuous and instantaneous sampling and two methods to deal with animals out of sight on dairy cattle behavior and interaction with their calf in the first hours post-partum

Manfrè,

Battini,

Simonetto

et al. 2024

Front. Vet. Sci.

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Animal behavior can provide useful information about animal welfare, but methods and tools used to gather behavioral data and data treatment can influence the results. Therefore, this study was carried out on dairy cow (Bos taurus) behavior and interaction with calves early post-partum aiming at comparing two sampling rules, namely continuous and instantaneous sampling at scan intervals of 30 s, 1, 2, 3, 4, 5, and 10 min, and of two methods to deal with out of sight animals. The study was based on three assumptions: (1) continuous sampling provides the most complete and accurate data, allowing the observation of seldom behaviors and short events; (2) instantaneous sampling can provide accurate measurements of frequency and duration, especially at short scan intervals; (3) differences in behavioral results may occur depending on whether a correction for time out of sight is applied or not. Thus, 10 dams were observed from videos in the 2 h post-parturition. Ruminating, stereotypies, calf-biting and calf-butting were not recorded during the observation period. Other behaviors were observed only with continuous sampling or with continuous and instantaneous at 30-s scan intervals. The recoding of several behaviors was less accurate when applying longer scan intervals. Data from continuous and instantaneous sampling at 30-s scan intervals were compared with Wilcoxon test. Results showed no significant differences for posture, position in the pen and all behaviors (p > 0.05) except vocalizing (p = 0.003). The same test did not highlight significant differences due to method of dealing with out of sight for both sampling rules (p > 0.05). Correlation between continuous and instantaneous sampling were prevalently high at 30-s intervals and they decreased as the length of scan intervals increased for most behaviors. Results confirmed the first two assumptions suggesting that continuous sampling is more accurate, in particular for short and rare behaviors, and caution against the suitability of dam behavioral data collected using instantaneous sampling at scan intervals of minutes. The third assumption was not proven by this study. Results should be considered in light of the development of new technologies that relies on data acquired by sensors and imaging to monitor cow-calf welfare and behavior post-parturition.

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“…Every vocalization, from the melodious song of a bird to the distressed call of a confined animal, carries with it layers of information [15,16]. Advanced acoustic analysis tools, powered by cognitive computing algorithms, are now decoding these vocalizations, unveiling hidden cues about animal emotions, intentions, and even cognitive processes [17,18].…”

Section: Sound Vocalization: Unveiling the Acoustic Spectrum Of Anima...mentioning

confidence: 99%

Decoding the Digital Barnyard - Cognitive Computing in Farm Animal Emotions and Welfare

Neethirajan

2023

Preprint

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In this paper, we critically examine the burgeoning role of advanced computational methodologies in deciphering the complex tapestry of farm animal behaviors and emotions. Leveraging digital imaging and artificial intelligence, we unearth nuanced behavioral patterns and micro-expressions, offering predictive insights into animal emotional states. Sound vocalization analysis, often overlooked, emerges as a pivotal tool, decoding intricate communicative nuances and emotional undertones. Cognitive tests, including mirror and bias assessments, challenge long-standing perceptions, revealing surprising depths of animal self-awareness and cognitive sophistication. However, the paper also underscores the imperative of integrating these tools with a profound understanding of animal psyche, ensuring technology serves as an enhancer, not a replacement, of traditional observational methods. This research not only highlights the transformative potential of cognitive computing in animal welfare but also calls for a judicious application, ensuring technology augments, not undermines, the intrinsic value of human-animal interactions and understanding.

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Automatic detection of cow/calf vocalizations in free-stall barn

Cited by 5 publications

References 24 publications

Affective State Recognition in Livestock—Artificial Intelligence Approaches

Affective State Recognition in Livestock—Artificial Intelligence Approaches

A methodological approach to compare continuous and instantaneous sampling and two methods to deal with animals out of sight on dairy cattle behavior and interaction with their calf in the first hours post-partum

Decoding the Digital Barnyard - Cognitive Computing in Farm Animal Emotions and Welfare

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